--------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /Users/ignaciojurado/Dropbox/My Mac (MacBook-Pro.home)/Downloads/dataverse_files (1)/
> Replication_Appendix.log
  log type:  text
 opened on:  10 Jul 2023, 18:11:33

. 
. 
. ***************************
. ******************************************
. *******************APPENDIX**************
. ***************************************
. ***************************
. 
. use "Data_Officers_replication.dta", clear

. 
. 
. replace voteimp_w1=.  if  voteimp_w1 ==99
(76 real changes made, 76 to missing)

. replace voteimp_w2=.  if  voteimp_w2 ==99
(59 real changes made, 59 to missing)

. replace voteimp_w3=.  if  voteimp_w3 ==99
(53 real changes made, 53 to missing)

. 
. gen d_voteimp_may=voteimp_w2-voteimp_w1
(396 missing values generated)

. gen d_voteimp_nov=voteimp_w3-voteimp_w1
(794 missing values generated)

. 
. replace voteduty_w1=. if voteduty_w1 ==99
(55 real changes made, 55 to missing)

. replace voteduty_w2=. if voteduty_w2 ==99
(38 real changes made, 38 to missing)

. replace voteduty_w3=. if voteduty_w3 ==99
(42 real changes made, 42 to missing)

. 
. gen d_voteduty_may=voteduty_w2-voteduty_w1
(366 missing values generated)

. gen d_voteduty_nov=voteduty_w3-voteduty_w1
(776 missing values generated)

. 
. replace cleanelections_w1=.  if  cleanelections_w1==99
(103 real changes made, 103 to missing)

. replace cleanelections_w2=.  if  cleanelections_w2==99
(74 real changes made, 74 to missing)

. replace cleanelections_w3=.  if  cleanelections_w3==99
(54 real changes made, 54 to missing)

. 
. gen d_cleanelections_may=cleanelections_w2-cleanelections_w1
(433 missing values generated)

. gen d_cleanelections_nov=cleanelections_w3-cleanelections_w1
(811 missing values generated)

. 
. replace trustparties_w1=. if  trustparties_w1==99
(69 real changes made, 69 to missing)

. replace trustparties_w2=. if  trustparties_w2==99
(49 real changes made, 49 to missing)

. replace trustparties_w3=. if  trustparties_w3==99
(42 real changes made, 42 to missing)

. 
. gen d_trustparties_may=trustparties_w2-trustparties_w1
(389 missing values generated)

. gen d_trustparties_nov=trustparties_w3-trustparties_w1
(790 missing values generated)

. 
. replace  freedomideas_w1=. if freedomideas_w1==99
(69 real changes made, 69 to missing)

. replace  freedomideas_w2=. if freedomideas_w2==99
(53 real changes made, 53 to missing)

. replace  freedomideas_w3=. if freedomideas_w3==99
(51 real changes made, 51 to missing)

. 
. gen d_freedomideas_may=freedomideas_w2-freedomideas_w1
(388 missing values generated)

. gen d_freedomideas_nov=freedomideas_w3-freedomideas_w1
(790 missing values generated)

. 
. replace citizensimp_w1=. if citizensimp_w1==99
(54 real changes made, 54 to missing)

. replace citizensimp_w2=. if citizensimp_w2==99
(46 real changes made, 46 to missing)

. replace citizensimp_w3=. if citizensimp_w3==99
(38 real changes made, 38 to missing)

. 
. gen d_citizensimp_may=citizensimp_w2-citizensimp_w1
(370 missing values generated)

. gen d_citizensimp_nov=citizensimp_w3-citizensimp_w1
(770 missing values generated)

. 
. replace votenotimp_w1=. if votenotimp_w1==99
(54 real changes made, 54 to missing)

. replace votenotimp_w2=. if votenotimp_w2==99
(46 real changes made, 46 to missing)

. replace votenotimp_w3=. if votenotimp_w3==99
(44 real changes made, 44 to missing)

. 
. gen d_votenotimp_may=votenotimp_w2-votenotimp_w1
(370 missing values generated)

. gen d_votenotimp_nov=votenotimp_w3-votenotimp_w1
(774 missing values generated)

. 
. replace corruption_w1=. if   corruption_w1==99
(62 real changes made, 62 to missing)

. replace corruption_w2=. if    corruption_w2==99
(47 real changes made, 47 to missing)

. replace corruption_w3=. if    corruption_w3==99
(42 real changes made, 42 to missing)

. 
. gen d_corruption_may= corruption_w2-corruption_w1
(378 missing values generated)

. gen d_corruption_nov= corruption_w3-corruption_w1
(777 missing values generated)

. 
. replace dontunderstandpolitics_w1=. if   dontunderstandpolitics_w1==99
(75 real changes made, 75 to missing)

. replace dontunderstandpolitics_w2=. if    dontunderstandpolitics_w2==99
(54 real changes made, 54 to missing)

. replace dontunderstandpolitics_w3=. if    dontunderstandpolitics_w3==99
(46 real changes made, 46 to missing)

. 
. gen d_dontunderstandpolitics_may=dontunderstandpolitics_w2-dontunderstandpolitics_w1
(390 missing values generated)

. gen d_dontunderstandpolitics_nov=dontunderstandpolitics_w3-dontunderstandpolitics_w1
(791 missing values generated)

. 
. replace demonotworking_w1=. if   demonotworking_w1==99
(68 real changes made, 68 to missing)

. replace demonotworking_w2=. if    demonotworking_w2==99
(53 real changes made, 53 to missing)

. replace demonotworking_w3=. if    demonotworking_w3==99
(37 real changes made, 37 to missing)

. 
. gen d_demonotworking_may=demonotworking_w2-demonotworking_w1
(393 missing values generated)

. gen d_demonotworking_nov=demonotworking_w3-demonotworking_w1
(783 missing values generated)

. 
. replace systnotworried_w1=. if    systnotworried_w1==99
(71 real changes made, 71 to missing)

. replace systnotworried_w2=. if    systnotworried_w2==99
(56 real changes made, 56 to missing)

. replace systnotworried_w3=. if    systnotworried_w3==99
(46 real changes made, 46 to missing)

. 
. gen d_systnotworried_may=systnotworried_w2-systnotworried_w1
(391 missing values generated)

. gen d_systnotworried_nov=systnotworried_w3-systnotworried_w1
(786 missing values generated)

. 
. 
. 
. *******************************
. * A.1. DESCRIPTIVES
. ***************************
. 
. *Summary statistics
. 
. **We use the Wave 2 sample that we use in the analyses
. 
. eststo: logit  vote_may  treatment  female age low_education high_educat i.employmentstatus  , 

Iteration 0:   log likelihood = -747.73911  
Iteration 1:   log likelihood = -722.29121  
Iteration 2:   log likelihood = -721.54128  
Iteration 3:   log likelihood = -721.54024  
Iteration 4:   log likelihood = -721.54024  

Logistic regression                                     Number of obs =  1,598
                                                        LR chi2(9)    =  52.40
                                                        Prob > chi2   = 0.0000
Log likelihood = -721.54024                             Pseudo R2     = 0.0350

----------------------------------------------------------------------------------
        vote_may | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       treatment |   .6150714   .2030021     3.03   0.002     .2171945    1.012948
          female |    .002777   .1420647     0.02   0.984    -.2756646    .2812186
             age |   .0343318   .0068791     4.99   0.000      .020849    .0478145
   low_education |  -.5623431   .2224401    -2.53   0.011    -.9983178   -.1263685
  high_education |   .2069719   .1508254     1.37   0.170    -.0886405    .5025843
                 |
employmentstatus |
        Retired  |    .047678   .3265628     0.15   0.884    -.5923732    .6877293
     Unemployed  |  -.1040245   .1969586    -0.53   0.597    -.4900562    .2820073
        Student  |   .2655284   .2398783     1.11   0.268    -.2046245    .7356812
    Housechores  |  -.8591972   .3767057    -2.28   0.023    -1.597527   -.1208677
                 |
           _cons |   .0455785   .3026591     0.15   0.880    -.5476225    .6387795
----------------------------------------------------------------------------------
(est1 stored)

. gen sample_may=1 if e(sample)
(983 missing values generated)

. 
. 
. sum treatment   vote_2016 vote_april vote_may vote_nov female age low_educ med_ed high_ed ///
>  employed retired unemployed student housechores ideology if sample_m==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
   treatment |      1,598    .1745932    .3797376          0          1
   vote_2016 |      1,598    .8585732    .3485703          0          1
  vote_april |      1,598    .8911139    .3115938          0          1
    vote_may |      1,598    .8222778    .3823983          0          1
vote_novem~r |      1,302    .8402458    .3665187          0          1
-------------+---------------------------------------------------------
      female |      1,598    .4755945    .4995603          0          1
         age |      1,598    41.76658     12.4769         18         65
low_educat~n |      1,598    .0876095    .2828147          0          1
med_educat~n |      1,598     .563204    .4961444          0          1
high_educa~n |      1,598    .3491865    .4768622          0          1
-------------+---------------------------------------------------------
    employed |      1,598    .6708385    .4700558          0          1
     retired |      1,598     .068836    .2532544          0          1
  unemployed |      1,598    .1345432    .3413418          0          1
     student |      1,598    .1007509    .3010929          0          1
 housechores |      1,598    .0250313     .156269          0          1
-------------+---------------------------------------------------------
    ideology |      1,405    4.320285    2.036328          0         10

. 
. 
.   
. *A.2 T-TEST 
. 
. ttest vote_2016, by(treatment)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. err.   Std. dev.   [95% conf. interval]
---------+--------------------------------------------------------------------
       0 |   1,513    .8532716    .0090997    .3539519    .8354224    .8711209
       1 |     279     .874552    .0198656    .3318214    .8354458    .9136581
---------+--------------------------------------------------------------------
Combined |   1,792    .8565848     .008282    .3505936    .8403414    .8728282
---------+--------------------------------------------------------------------
    diff |           -.0212803    .0228438               -.0660835    .0235229
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -0.9316
H0: diff = 0                                     Degrees of freedom =     1790

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.1758         Pr(|T| > |t|) = 0.3517          Pr(T > t) = 0.8242

. ttest vote_april, by( treatment)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. err.   Std. dev.   [95% conf. interval]
---------+--------------------------------------------------------------------
       0 |   1,319    .8764215    .0090651    .3292249     .858638     .894205
       1 |     279    .9605735    .0116718    .1949572    .9375972    .9835498
---------+--------------------------------------------------------------------
Combined |   1,598    .8911139    .0077947    .3115938    .8758249    .9064028
---------+--------------------------------------------------------------------
    diff |           -.0841519    .0204311               -.1242266   -.0440772
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -4.1188
H0: diff = 0                                     Degrees of freedom =     1596

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0000         Pr(|T| > |t|) = 0.0000          Pr(T > t) = 1.0000

. ttest vote_may, by(treatment)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. err.   Std. dev.   [95% conf. interval]
---------+--------------------------------------------------------------------
       0 |   1,319    .8097043    .0108123    .3926833     .788493    .8309156
       1 |     279    .8817204    .0193686    .3235192    .8435927    .9198482
---------+--------------------------------------------------------------------
Combined |   1,598    .8222778    .0095659    .3823983    .8035147     .841041
---------+--------------------------------------------------------------------
    diff |           -.0720161    .0251422               -.1213312    -.022701
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -2.8644
H0: diff = 0                                     Degrees of freedom =     1596

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0021         Pr(|T| > |t|) = 0.0042          Pr(T > t) = 0.9979

. ttest vote_nov, by( treatment)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. err.   Std. dev.   [95% conf. interval]
---------+--------------------------------------------------------------------
       0 |   1,072    .8386194    .0112412    .3680533    .8165621    .8606767
       1 |     230    .8478261    .0237359    .3599728    .8010574    .8945948
---------+--------------------------------------------------------------------
Combined |   1,302    .8402458    .0101576    .3665187    .8203187    .8601728
---------+--------------------------------------------------------------------
    diff |           -.0092067    .0266433               -.0614752    .0430618
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -0.3456
H0: diff = 0                                     Degrees of freedom =     1300

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.3649         Pr(|T| > |t|) = 0.7297          Pr(T > t) = 0.6351

. 
. 
. 
. **********
. *BALANCE : REGRESION
. ***********
. 
. 
. eststo clear

. eststo: logit treatment  vote_2016  ideology low_educ high_educ   voteimp_w1  voteduty_w1  clean
> elections_w1 trustparties_w1  votenotimp_w1 citizensimp_w1   freedomideas_w1  corruption_w1 dont
> understandpolitics_w1 demonotworking_w1  systnotworried_w1 

Iteration 0:   log likelihood = -649.55687  
Iteration 1:   log likelihood = -639.47447  
Iteration 2:   log likelihood =  -639.3169  
Iteration 3:   log likelihood = -639.31677  
Iteration 4:   log likelihood = -639.31677  

Logistic regression                                     Number of obs =  1,481
                                                        LR chi2(15)   =  20.48
                                                        Prob > chi2   = 0.1543
Log likelihood = -639.31677                             Pseudo R2     = 0.0158

-------------------------------------------------------------------------------------------
                treatment | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
--------------------------+----------------------------------------------------------------
                vote_2016 |   .0938799   .2321001     0.40   0.686     -.361028    .5487878
                 ideology |  -.0135734   .0368775    -0.37   0.713    -.0858519    .0587052
            low_education |  -.4703586   .3245069    -1.45   0.147     -1.10638    .1656631
           high_education |   .2466113   .1512152     1.63   0.103    -.0497651    .5429876
               voteimp_w1 |   .0448381   .0317155     1.41   0.157    -.0173231    .1069993
              voteduty_w1 |  -.0275814   .0315422    -0.87   0.382     -.089403    .0342402
        cleanelections_w1 |   .0430431   .0271213     1.59   0.112    -.0101137    .0961999
          trustparties_w1 |   .0106793    .033334     0.32   0.749    -.0546542    .0760129
            votenotimp_w1 |   .0133265   .0265652     0.50   0.616    -.0387403    .0653934
           citizensimp_w1 |   -.010264   .0257359    -0.40   0.690    -.0607054    .0401774
          freedomideas_w1 |   .0384657   .0278797     1.38   0.168    -.0161775     .093109
            corruption_w1 |  -.0484318   .0310512    -1.56   0.119     -.109291    .0124273
dontunderstandpolitics_w1 |   .0393814   .0266824     1.48   0.140    -.0129151     .091678
        demonotworking_w1 |   -.023111   .0268619    -0.86   0.390    -.0757593    .0295373
        systnotworried_w1 |   .0287874   .0318683     0.90   0.366    -.0336734    .0912482
                    _cons |   -2.29413   .5089373    -4.51   0.000    -3.291628   -1.296631
-------------------------------------------------------------------------------------------
(est1 stored)

. 
. esttab using Balance.tex, label  ///
>    title(Balance) replace se
(file Balance.tex not found)
(output written to Balance.tex)

. 
.    
. ***************
. *ATTTRITION
. *****************
. 
. 
. gen attrition1=.
(2,581 missing values generated)

. replace attrition1=0 if wave1==1 & wave2_w2==1
(2,289 real changes made)

. replace attrition1=1 if wave1==1 & wave2_w2==.
(292 real changes made)

. 
. gen attrition2=.
(2,581 missing values generated)

. replace attrition2=1 if wave2_w2==1 & wave3_w3==.
(421 real changes made)

. replace attrition2=0 if wave2_w2==1 & wave3_w3==1
(1,868 real changes made)

. 
. 
. 
. eststo clear

. 
. 
. eststo: reg attrition1 female age low_ed high_ed  i.employmentstatus vote_2016  ideology  i.regi
> on

      Source |       SS           df       MS      Number of obs   =     2,246
-------------+----------------------------------   F(26, 2219)     =      1.30
       Model |  3.33803726        26  .128386048   Prob > F        =    0.1408
    Residual |  218.834714     2,219  .098618618   R-squared       =    0.0150
-------------+----------------------------------   Adj R-squared   =    0.0035
       Total |  222.172752     2,245  .098963364   Root MSE        =    .31404

----------------------------------------------------------------------------------
      attrition1 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
          female |  -.0007898   .0142628    -0.06   0.956    -.0287595      .02718
             age |  -.0020828   .0006827    -3.05   0.002    -.0034216   -.0007439
   low_education |   .0302847   .0257923     1.17   0.240    -.0202949    .0808643
  high_education |  -.0139647    .014562    -0.96   0.338    -.0425211    .0145918
                 |
employmentstatus |
        Retired  |  -.0339844   .0299681    -1.13   0.257    -.0927529    .0247841
     Unemployed  |  -.0177057   .0205939    -0.86   0.390    -.0580909    .0226796
        Student  |  -.0035756   .0259049    -0.14   0.890     -.054376    .0472247
    Housechores  |    .025926   .0493554     0.53   0.599    -.0708616    .1227136
                 |
       vote_2016 |   .0013712   .0201282     0.07   0.946    -.0381009    .0408432
        ideology |  -.0047635   .0033251    -1.43   0.152    -.0112841     .001757
                 |
          region |
              2  |  -.0584426   .0407456    -1.43   0.152    -.1383462     .021461
              3  |  -.0408719    .045474    -0.90   0.369    -.1300479    .0483042
              4  |     .02753   .0470044     0.59   0.558    -.0646472    .1197072
              5  |  -.0630319   .0391128    -1.61   0.107    -.1397335    .0136696
              6  |  -.0319484   .0572963    -0.56   0.577    -.1443083    .0804115
              7  |  -.0000618   .0301234    -0.00   0.998    -.0591348    .0590112
              8  |   .0131272   .0361981     0.36   0.717    -.0578586    .0841129
              9  |  -.0225703   .0239952    -0.94   0.347    -.0696257    .0244852
             10  |  -.0374851   .0262326    -1.43   0.153     -.088928    .0139579
             11  |  -.0584726   .0457377    -1.28   0.201    -.1481657    .0312206
             12  |   .0091388   .0313956     0.29   0.771     -.052429    .0707066
             13  |  -.0211316   .0236923    -0.89   0.373    -.0675931    .0253299
             14  |  -.0086585   .0419534    -0.21   0.837    -.0909304    .0736135
             15  |  -.0749502   .0580437    -1.29   0.197    -.1887759    .0388755
             16  |  -.0453612   .0351792    -1.29   0.197    -.1143489    .0236264
             17  |  -.0661326    .085611    -0.77   0.440    -.2340187    .1017534
                 |
           _cons |    .243092   .0396082     6.14   0.000      .165419     .320765
----------------------------------------------------------------------------------
(est1 stored)

. 
. eststo: reg attrition2 female age  low_ed  high_ed   i.employmentstatus vote_2016  ideology  i.r
> egion

      Source |       SS           df       MS      Number of obs   =     1,996
-------------+----------------------------------   F(26, 1969)     =      2.10
       Model |  7.85762285        26  .302216263   Prob > F        =    0.0009
    Residual |  282.713018     1,969  .143582031   R-squared       =    0.0270
-------------+----------------------------------   Adj R-squared   =    0.0142
       Total |  290.570641     1,995  .145649444   Root MSE        =    .37892

----------------------------------------------------------------------------------
      attrition2 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
          female |  -.0156048   .0182911    -0.85   0.394    -.0514767    .0202671
             age |  -.0028349   .0008772    -3.23   0.001    -.0045552   -.0011146
   low_education |   .0539256   .0334324     1.61   0.107     -.011641    .1194922
  high_education |   .0122695   .0186774     0.66   0.511    -.0243601    .0488991
                 |
employmentstatus |
        Retired  |   -.019206   .0373409    -0.51   0.607    -.0924379    .0540258
     Unemployed  |  -.0379638   .0262398    -1.45   0.148    -.0894245    .0134968
        Student  |   .0323462   .0338894     0.95   0.340    -.0341166    .0988091
    Housechores  |   .0422384    .064023     0.66   0.509    -.0833215    .1677983
                 |
       vote_2016 |  -.0355731   .0257952    -1.38   0.168    -.0861619    .0150157
        ideology |   .0025097   .0042237     0.59   0.552    -.0057736    .0107931
                 |
          region |
              2  |   .0048903   .0513154     0.10   0.924     -.095748    .1055285
              3  |   .1147485   .0577901     1.99   0.047     .0014124    .2280847
              4  |   .0925396   .0616728     1.50   0.134    -.0284112    .2134905
              5  |   .1131745   .0491078     2.30   0.021     .0168658    .2094831
              6  |  -.0968403   .0727383    -1.33   0.183    -.2394924    .0458119
              7  |  -.0031307   .0390216    -0.08   0.936    -.0796586    .0733973
              8  |   .0272846   .0470032     0.58   0.562    -.0648966    .1194659
              9  |    .064534   .0308987     2.09   0.037     .0039363    .1251316
             10  |   .0546325   .0336071     1.63   0.104    -.0112768    .1205418
             11  |    .035024   .0576069     0.61   0.543    -.0779528    .1480008
             12  |  -.0148166   .0409174    -0.36   0.717    -.0950626    .0654294
             13  |   .0569998   .0304119     1.87   0.061    -.0026432    .1166427
             14  |   .0782058   .0540539     1.45   0.148    -.0278031    .1842146
             15  |   -.018245   .0726228    -0.25   0.802    -.1606706    .1241805
             16  |   .0054743   .0447768     0.12   0.903    -.0823405    .0932891
             17  |  -.0657423   .1073283    -0.61   0.540    -.2762313    .1447468
                 |
           _cons |   .2808175    .051807     5.42   0.000     .1792152    .3824198
----------------------------------------------------------------------------------
(est2 stored)

.  
.  esttab using Attrition.tex, label  ///
>    title(Attriton) replace se
(output written to Attrition.tex)

. 
. 
. 
.    
. ************************
. *******FULL TABLES
. ************************
. 
. eststo clear

. 
. eststo: logit  vote_2016 treatment  female age low_education high_educat i.employmentstatus  

Iteration 0:   log likelihood = -736.71793  
Iteration 1:   log likelihood = -724.00487  
Iteration 2:   log likelihood = -723.59261  
Iteration 3:   log likelihood = -723.59075  
Iteration 4:   log likelihood = -723.59075  

Logistic regression                                     Number of obs =  1,792
                                                        LR chi2(9)    =  26.25
                                                        Prob > chi2   = 0.0019
Log likelihood = -723.59075                             Pseudo R2     = 0.0178

----------------------------------------------------------------------------------
       vote_2016 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       treatment |   .2444603   .1982222     1.23   0.217    -.1440481    .6329686
          female |   .1699624   .1446739     1.17   0.240    -.1135932     .453518
             age |   .0166896   .0068194     2.45   0.014     .0033238    .0300554
   low_education |  -.5035619   .2252953    -2.24   0.025    -.9451326   -.0619913
  high_education |   -.166789   .1501156    -1.11   0.267    -.4610101    .1274321
                 |
employmentstatus |
        Retired  |   .5904522   .3904844     1.51   0.131    -.1748833    1.355788
     Unemployed  |  -.2469715   .1928545    -1.28   0.200    -.6249595    .1310165
        Student  |   .4023675   .2601935     1.55   0.122    -.1076024    .9123373
    Housechores  |  -.3304033   .4158192    -0.79   0.427    -1.145394    .4845873
                 |
           _cons |   1.086706   .3051169     3.56   0.000     .4886881    1.684724
----------------------------------------------------------------------------------
(est1 stored)

. eststo: logit  vote_april  treatment  female age low_education high_educat i.employmentstatus  ,
>  

Iteration 0:   log likelihood = -549.99983  
Iteration 1:   log likelihood = -528.14285  
Iteration 2:   log likelihood = -526.48355  
Iteration 3:   log likelihood =  -526.4753  
Iteration 4:   log likelihood =  -526.4753  

Logistic regression                                     Number of obs =  1,598
                                                        LR chi2(9)    =  47.05
                                                        Prob > chi2   = 0.0000
Log likelihood = -526.4753                              Pseudo R2     = 0.0428

----------------------------------------------------------------------------------
      vote_april | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       treatment |   1.254747   .3225147     3.89   0.000     .6226296    1.886864
          female |   .0400179   .1735128     0.23   0.818    -.3000609    .3800967
             age |   .0357098   .0083902     4.26   0.000     .0192654    .0521542
   low_education |  -.5034711   .2635073    -1.91   0.056    -1.019936    .0129939
  high_education |   .1977487   .1859846     1.06   0.288    -.1667744    .5622718
                 |
employmentstatus |
        Retired  |  -.4663483   .3503935    -1.33   0.183    -1.153107    .2204103
     Unemployed  |   -.239351   .2326141    -1.03   0.303    -.6952663    .2165644
        Student  |   .3684067   .2950318     1.25   0.212     -.209845    .9466584
    Housechores  |  -.3942728   .5142186    -0.77   0.443    -1.402123    .6135772
                 |
           _cons |   .5220833   .3645134     1.43   0.152    -.1923498    1.236516
----------------------------------------------------------------------------------
(est2 stored)

. eststo: logit   vote_may  treatment  female age low_education high_educat i.employmentstatus  , 

Iteration 0:   log likelihood = -747.73911  
Iteration 1:   log likelihood = -722.29121  
Iteration 2:   log likelihood = -721.54128  
Iteration 3:   log likelihood = -721.54024  
Iteration 4:   log likelihood = -721.54024  

Logistic regression                                     Number of obs =  1,598
                                                        LR chi2(9)    =  52.40
                                                        Prob > chi2   = 0.0000
Log likelihood = -721.54024                             Pseudo R2     = 0.0350

----------------------------------------------------------------------------------
        vote_may | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       treatment |   .6150714   .2030021     3.03   0.002     .2171945    1.012948
          female |    .002777   .1420647     0.02   0.984    -.2756646    .2812186
             age |   .0343318   .0068791     4.99   0.000      .020849    .0478145
   low_education |  -.5623431   .2224401    -2.53   0.011    -.9983178   -.1263685
  high_education |   .2069719   .1508254     1.37   0.170    -.0886405    .5025843
                 |
employmentstatus |
        Retired  |    .047678   .3265628     0.15   0.884    -.5923732    .6877293
     Unemployed  |  -.1040245   .1969586    -0.53   0.597    -.4900562    .2820073
        Student  |   .2655284   .2398783     1.11   0.268    -.2046245    .7356812
    Housechores  |  -.8591972   .3767057    -2.28   0.023    -1.597527   -.1208677
                 |
           _cons |   .0455785   .3026591     0.15   0.880    -.5476225    .6387795
----------------------------------------------------------------------------------
(est3 stored)

. eststo: logit   vote_nov  treatment  female age low_education high_educat i.employmentstatus  

Iteration 0:   log likelihood = -571.91926  
Iteration 1:   log likelihood = -548.19902  
Iteration 2:   log likelihood = -546.54231  
Iteration 3:   log likelihood = -546.53664  
Iteration 4:   log likelihood = -546.53664  

Logistic regression                                     Number of obs =  1,302
                                                        LR chi2(9)    =  50.77
                                                        Prob > chi2   = 0.0000
Log likelihood = -546.53664                             Pseudo R2     = 0.0444

----------------------------------------------------------------------------------
   vote_november | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       treatment |   .1035936   .2085536     0.50   0.619     -.305164    .5123512
          female |   .0586665   .1676649     0.35   0.726    -.2699507    .3872838
             age |    .025537   .0078597     3.25   0.001     .0101323    .0409417
   low_education |   -.805846   .2514392    -3.20   0.001    -1.298658   -.3130343
  high_education |   .2130898   .1778527     1.20   0.231     -.135495    .5616746
                 |
employmentstatus |
        Retired  |   .4749288   .4243574     1.12   0.263    -.3567965    1.306654
     Unemployed  |  -.4902077   .2128173    -2.30   0.021     -.907322   -.0730935
        Student  |  -.2528901   .2790604    -0.91   0.365    -.7998383    .2940582
    Housechores  |  -1.169129   .3947928    -2.96   0.003    -1.942908   -.3953491
                 |
           _cons |   .6997127   .3548165     1.97   0.049      .004285     1.39514
----------------------------------------------------------------------------------
(est4 stored)

.  gen last_sample=1 if e(sample)
(1,279 missing values generated)

. esttab using Fullmodels.tex, label  ///
>    title(Full models) replace se
(file Fullmodels.tex not found)
(output written to Fullmodels.tex)

. 
.    
. *Only for November sample
. 
. eststo clear

. 
. eststo: logit  vote_2016 treatment  female age low_education high_educat i.employmentstatus  if 
> last_sample==1

Iteration 0:   log likelihood = -523.10696  
Iteration 1:   log likelihood = -515.55778  
Iteration 2:   log likelihood = -515.32722  
Iteration 3:   log likelihood = -515.32654  
Iteration 4:   log likelihood = -515.32654  

Logistic regression                                     Number of obs =  1,302
                                                        LR chi2(9)    =  15.56
                                                        Prob > chi2   = 0.0766
Log likelihood = -515.32654                             Pseudo R2     = 0.0149

----------------------------------------------------------------------------------
       vote_2016 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       treatment |   .0547876   .2141656     0.26   0.798    -.3649693    .4745446
          female |   .2266387   .1746095     1.30   0.194    -.1155895    .5688669
             age |   .0084353   .0080308     1.05   0.294    -.0073049    .0241754
   low_education |  -.4492166    .280445    -1.60   0.109    -.9988787    .1004455
  high_education |  -.1819944   .1788101    -1.02   0.309    -.5324557     .168467
                 |
employmentstatus |
        Retired  |   .5538844   .4223283     1.31   0.190    -.2738639    1.381633
     Unemployed  |  -.3431607   .2240702    -1.53   0.126    -.7823302    .0960088
        Student  |   .3148381   .3358588     0.94   0.349     -.343433    .9731092
    Housechores  |  -.2479457   .4773581    -0.52   0.603     -1.18355    .6876589
                 |
           _cons |   1.474019     .37152     3.97   0.000      .745853    2.202185
----------------------------------------------------------------------------------
(est1 stored)

. eststo: logit  vote_april  treatment  female age low_education high_educat i.employmentstatus   
> if last_sample==1 , 

Iteration 0:   log likelihood =  -416.1829  
Iteration 1:   log likelihood = -400.60184  
Iteration 2:   log likelihood = -399.20596  
Iteration 3:   log likelihood = -399.19916  
Iteration 4:   log likelihood = -399.19916  

Logistic regression                                     Number of obs =  1,302
                                                        LR chi2(9)    =  33.97
                                                        Prob > chi2   = 0.0001
Log likelihood = -399.19916                             Pseudo R2     = 0.0408

----------------------------------------------------------------------------------
      vote_april | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       treatment |   1.278046   .3770288     3.39   0.001     .5390832    2.017009
          female |   .0578287   .2032588     0.28   0.776    -.3405513    .4562086
             age |   .0333939   .0096003     3.48   0.001     .0145775    .0522102
   low_education |  -.7004864   .3008828    -2.33   0.020    -1.290206   -.1107669
  high_education |   .1358636   .2160489     0.63   0.529    -.2875844    .5593116
                 |
employmentstatus |
        Retired  |   -.270973   .3995367    -0.68   0.498    -1.054051    .5121046
     Unemployed  |  -.0303361    .275664    -0.11   0.912    -.5706275    .5099553
        Student  |   .5705425   .3793753     1.50   0.133    -.1730194    1.314104
    Housechores  |  -.3659835   .5732709    -0.64   0.523    -1.489574    .7576068
                 |
           _cons |    .684267   .4245723     1.61   0.107    -.1478794    1.516413
----------------------------------------------------------------------------------
(est2 stored)

. eststo: logit   vote_may  treatment  female age low_education high_educat i.employmentstatus   i
> f last_sample==1, 

Iteration 0:   log likelihood = -589.83854  
Iteration 1:   log likelihood = -570.22913  
Iteration 2:   log likelihood = -569.59654  
Iteration 3:   log likelihood = -569.59582  
Iteration 4:   log likelihood = -569.59582  

Logistic regression                                     Number of obs =  1,302
                                                        LR chi2(9)    =  40.49
                                                        Prob > chi2   = 0.0000
Log likelihood = -569.59582                             Pseudo R2     = 0.0343

----------------------------------------------------------------------------------
        vote_may | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       treatment |   .4070883   .2173507     1.87   0.061    -.0189113    .8330879
          female |   .0031326   .1626888     0.02   0.985    -.3157315    .3219967
             age |   .0348089   .0077296     4.50   0.000     .0196592    .0499586
   low_education |  -.6776749   .2525315    -2.68   0.007    -1.172628   -.1827223
  high_education |   .2487135   .1719056     1.45   0.148    -.0882152    .5856422
                 |
employmentstatus |
        Retired  |   .0447341   .3567495     0.13   0.900    -.6544822    .7439503
     Unemployed  |  -.0327198   .2239614    -0.15   0.884     -.471676    .4062364
        Student  |   .1942779   .2806001     0.69   0.489    -.3556881    .7442439
    Housechores  |   -.841852   .4107964    -2.05   0.040    -1.646998   -.0367057
                 |
           _cons |   .0953787   .3439511     0.28   0.782    -.5787531    .7695105
----------------------------------------------------------------------------------
(est3 stored)

. eststo: logit   vote_nov  treatment  female age low_education high_educat i.employmentstatus   i
> f last_sample==1

Iteration 0:   log likelihood = -571.91926  
Iteration 1:   log likelihood = -548.19902  
Iteration 2:   log likelihood = -546.54231  
Iteration 3:   log likelihood = -546.53664  
Iteration 4:   log likelihood = -546.53664  

Logistic regression                                     Number of obs =  1,302
                                                        LR chi2(9)    =  50.77
                                                        Prob > chi2   = 0.0000
Log likelihood = -546.53664                             Pseudo R2     = 0.0444

----------------------------------------------------------------------------------
   vote_november | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       treatment |   .1035936   .2085536     0.50   0.619     -.305164    .5123512
          female |   .0586665   .1676649     0.35   0.726    -.2699507    .3872838
             age |    .025537   .0078597     3.25   0.001     .0101323    .0409417
   low_education |   -.805846   .2514392    -3.20   0.001    -1.298658   -.3130343
  high_education |   .2130898   .1778527     1.20   0.231     -.135495    .5616746
                 |
employmentstatus |
        Retired  |   .4749288   .4243574     1.12   0.263    -.3567965    1.306654
     Unemployed  |  -.4902077   .2128173    -2.30   0.021     -.907322   -.0730935
        Student  |  -.2528901   .2790604    -0.91   0.365    -.7998383    .2940582
    Housechores  |  -1.169129   .3947928    -2.96   0.003    -1.942908   -.3953491
                 |
           _cons |   .6997127   .3548165     1.97   0.049      .004285     1.39514
----------------------------------------------------------------------------------
(est4 stored)

. esttab using Fullmodels2.tex, label  ///
>    title(Full models with Novemmber 2019 sample) replace se
(file Fullmodels2.tex not found)
(output written to Fullmodels2.tex)

. 
.    
.    
.    
. ************************ 
. * SOFT TREATMENT
. ************************
. 
. *MODELS WITHOUT COVARIATES
. 
. logit  vote_2016 replacement , 

Iteration 0:   log likelihood = -909.50936  
Iteration 1:   log likelihood = -909.39186  
Iteration 2:   log likelihood = -909.39184  

Logistic regression                                     Number of obs =  2,204
                                                        LR chi2(1)    =   0.24
                                                        Prob > chi2   = 0.6278
Log likelihood = -909.39184                             Pseudo R2     = 0.0001

------------------------------------------------------------------------------
   vote_2016 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
 replacement |    .063718   .1318197     0.48   0.629    -.1946438    .3220799
       _cons |   1.760495   .0726574    24.23   0.000     1.618089    1.902901
------------------------------------------------------------------------------

. margins, dydx( replacement) post

Average marginal effects                                 Number of obs = 2,204
Model VCE: OIM

Expression: Pr(vote_2016), predict()
dy/dx wrt:  replacement

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
 replacement |   .0078661   .0162736     0.48   0.629    -.0240295    .0397618
------------------------------------------------------------------------------

. estimates store s1

. 
. 
. logit  vote_ap    replacement , 

Iteration 0:   log likelihood = -712.85573  
Iteration 1:   log likelihood = -711.04872  
Iteration 2:   log likelihood = -711.04166  
Iteration 3:   log likelihood = -711.04166  

Logistic regression                                     Number of obs =  2,010
                                                        LR chi2(1)    =   3.63
                                                        Prob > chi2   = 0.0568
Log likelihood = -711.04166                             Pseudo R2     = 0.0025

------------------------------------------------------------------------------
  vote_april | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
 replacement |   .2891261   .1541153     1.88   0.061    -.0129344    .5911866
       _cons |   1.958971   .0836661    23.41   0.000     1.794988    2.122953
------------------------------------------------------------------------------

. margins, dydx(  replacement ) post

Average marginal effects                                 Number of obs = 2,010
Model VCE: OIM

Expression: Pr(vote_april), predict()
dy/dx wrt:  replacement

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
 replacement |    .029136   .0155581     1.87   0.061    -.0013574    .0596294
------------------------------------------------------------------------------

. estimates store s2

. 
. 
. logit   vote_may    replacement , 

Iteration 0:   log likelihood = -946.28675  
Iteration 1:   log likelihood = -944.77715  
Iteration 2:   log likelihood = -944.77481  
Iteration 3:   log likelihood = -944.77481  

Logistic regression                                     Number of obs =  2,010
                                                        LR chi2(1)    =   3.02
                                                        Prob > chi2   = 0.0820
Log likelihood = -944.77481                             Pseudo R2     = 0.0016

------------------------------------------------------------------------------
    vote_may | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
 replacement |   .2161803   .1254292     1.72   0.085    -.0296563    .4620169
       _cons |    1.44809   .0701455    20.64   0.000     1.310607    1.585573
------------------------------------------------------------------------------

. margins, dydx(   replacement ) post

Average marginal effects                                 Number of obs = 2,010
Model VCE: OIM

Expression: Pr(vote_may), predict()
dy/dx wrt:  replacement

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
 replacement |   .0318059   .0184385     1.72   0.085     -.004333    .0679448
------------------------------------------------------------------------------

. estimates store s3

. 
. 
. 
. logit   vote_nov   replacement , 

Iteration 0:   log likelihood = -704.93352  
Iteration 1:   log likelihood = -704.35797  
Iteration 2:   log likelihood = -704.35743  
Iteration 3:   log likelihood = -704.35743  

Logistic regression                                     Number of obs =  1,638
                                                        LR chi2(1)    =   1.15
                                                        Prob > chi2   = 0.2831
Log likelihood = -704.35743                             Pseudo R2     = 0.0008

-------------------------------------------------------------------------------
vote_november | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
  replacement |   .1561905   .1464592     1.07   0.286    -.1308643    .4432454
        _cons |   1.647991   .0830222    19.85   0.000     1.485271    1.810712
-------------------------------------------------------------------------------

. margins, dydx( replacement) post

Average marginal effects                                 Number of obs = 1,638
Model VCE: OIM

Expression: Pr(vote_november), predict()
dy/dx wrt:  replacement

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
 replacement |   .0203843   .0191124     1.07   0.286    -.0170753    .0578439
------------------------------------------------------------------------------

. estimates store s4

. 
. 
. coefplot (s1 , label( "Turnout 2016 (Placebo)")) ( s2, label( "Turnout April 2019")) ///
>  ( s3, label( "Turnout May 2019 (Short-term effect)")) ( s4, label("Turnout November 2019 (Long-
> term effect)")) ///
>  , keep (replacement) vertical yline(0) ylabel(-0.04(.04).2) xlabel("")  title ("Models without 
> covariates") legend(position(6))  ytitle("ATE")

. 
.  graph copy g1

.  
.  
.  ***************************
. *MODELS WITH COVARIATES
. ***************************
. 
. logit  vote_2016 replacement   female age low_education high_educat i.employmentstatus  , 

Iteration 0:   log likelihood = -909.50936  
Iteration 1:   log likelihood = -896.63238  
Iteration 2:   log likelihood = -896.34105  
Iteration 3:   log likelihood = -896.34086  
Iteration 4:   log likelihood = -896.34086  

Logistic regression                                     Number of obs =  2,204
                                                        LR chi2(9)    =  26.34
                                                        Prob > chi2   = 0.0018
Log likelihood = -896.34086                             Pseudo R2     = 0.0145

----------------------------------------------------------------------------------
       vote_2016 | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
     replacement |   .1091326   .1336587     0.82   0.414    -.1528337    .3710989
          female |   .0890783   .1301442     0.68   0.494    -.1659996    .3441562
             age |   .0165163   .0062401     2.65   0.008      .004286    .0287466
   low_education |   -.497236   .1999391    -2.49   0.013    -.8891094   -.1053627
  high_education |  -.2057302   .1347661    -1.53   0.127    -.4698669    .0584065
                 |
employmentstatus |
        Retired  |    .147909   .2969485     0.50   0.618    -.4340993    .7299173
     Unemployed  |   -.302681   .1697026    -1.78   0.074    -.6352919    .0299299
        Student  |   .4439258   .2438432     1.82   0.069    -.0339981    .9218497
    Housechores  |  -.5648157   .3809897    -1.48   0.138    -1.311542    .1819104
                 |
           _cons |   1.174414    .279828     4.20   0.000     .6259609    1.722866
----------------------------------------------------------------------------------

. margins, dydx( replacement) post

Average marginal effects                                 Number of obs = 2,204
Model VCE: OIM

Expression: Pr(vote_2016), predict()
dy/dx wrt:  replacement

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
 replacement |   .0133056   .0162953     0.82   0.414    -.0186325    .0452438
------------------------------------------------------------------------------

. estimates store s1

. 
. 
. logit  vote_ap    replacement  female age low_education high_educat i.employmentstatus  , 

Iteration 0:   log likelihood = -712.85573  
Iteration 1:   log likelihood = -691.72574  
Iteration 2:   log likelihood = -690.83569  
Iteration 3:   log likelihood = -690.83427  
Iteration 4:   log likelihood = -690.83427  

Logistic regression                                     Number of obs =  2,010
                                                        LR chi2(9)    =  44.04
                                                        Prob > chi2   = 0.0000
Log likelihood = -690.83427                             Pseudo R2     = 0.0309

----------------------------------------------------------------------------------
      vote_april | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
     replacement |   .3267528   .1572452     2.08   0.038     .0185578    .6349477
          female |   -.125969   .1520568    -0.83   0.407    -.4239948    .1720568
             age |   .0380129     .00744     5.11   0.000     .0234308    .0525949
   low_education |  -.4497051   .2304281    -1.95   0.051    -.9013359    .0019258
  high_education |   .2017074   .1619288     1.25   0.213    -.1156672    .5190821
                 |
employmentstatus |
        Retired  |   -.467778   .3092663    -1.51   0.130    -1.073929    .1383728
     Unemployed  |   -.408378   .1956827    -2.09   0.037     -.791909   -.0248471
        Student  |   .2872567   .2612394     1.10   0.272    -.2247631    .7992766
    Housechores  |  -.8328539   .4240176    -1.96   0.050    -1.663913   -.0017947
                 |
           _cons |   .5548065   .3231539     1.72   0.086    -.0785635    1.188176
----------------------------------------------------------------------------------

. margins, dydx(  replacement ) post

Average marginal effects                                 Number of obs = 2,010
Model VCE: OIM

Expression: Pr(vote_april), predict()
dy/dx wrt:  replacement

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
 replacement |   .0322073   .0155185     2.08   0.038     .0017917     .062623
------------------------------------------------------------------------------

. estimates store s2

. 
. 
. logit   vote_may    replacement  female age low_education high_educat i.employmentstatus  , 

Iteration 0:   log likelihood = -946.28675  
Iteration 1:   log likelihood = -915.75081  
Iteration 2:   log likelihood = -914.92725  
Iteration 3:   log likelihood = -914.92673  
Iteration 4:   log likelihood = -914.92673  

Logistic regression                                     Number of obs =  2,010
                                                        LR chi2(9)    =  62.72
                                                        Prob > chi2   = 0.0000
Log likelihood = -914.92673                             Pseudo R2     = 0.0331

----------------------------------------------------------------------------------
        vote_may | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
     replacement |   .2615895   .1286432     2.03   0.042     .0094533    .5137256
          female |  -.0476859   .1263319    -0.38   0.706    -.2952919    .1999202
             age |   .0356822   .0061843     5.77   0.000     .0235612    .0478032
   low_education |  -.5027895   .1934966    -2.60   0.009    -.8820358   -.1235432
  high_education |   .3470667   .1356997     2.56   0.011     .0811003    .6130331
                 |
employmentstatus |
        Retired  |  -.1833848   .2693892    -0.68   0.496    -.7113781    .3446084
     Unemployed  |   -.320657   .1654354    -1.94   0.053    -.6449044    .0035903
        Student  |   .2869023   .2203338     1.30   0.193    -.1449441    .7187486
    Housechores  |  -.8374205   .3607421    -2.32   0.020    -1.544462   -.1303791
                 |
           _cons |   .0121638   .2719247     0.04   0.964    -.5207988    .5451264
----------------------------------------------------------------------------------

. margins, dydx(   replacement ) post

Average marginal effects                                 Number of obs = 2,010
Model VCE: OIM

Expression: Pr(vote_may), predict()
dy/dx wrt:  replacement

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
 replacement |   .0372849   .0183037     2.04   0.042     .0014103    .0731595
------------------------------------------------------------------------------

. estimates store s3

. 
. 
. 
. logit   vote_nov   replacement  female age low_education high_educat i.employmentstatus  , 

Iteration 0:   log likelihood = -704.93352  
Iteration 1:   log likelihood = -678.72037  
Iteration 2:   log likelihood = -677.37458  
Iteration 3:   log likelihood = -677.37063  
Iteration 4:   log likelihood = -677.37063  

Logistic regression                                     Number of obs =  1,638
                                                        LR chi2(9)    =  55.13
                                                        Prob > chi2   = 0.0000
Log likelihood = -677.37063                             Pseudo R2     = 0.0391

----------------------------------------------------------------------------------
   vote_november | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
     replacement |   .1871184   .1501891     1.25   0.213    -.1072468    .4814835
          female |  -.0053987   .1495408    -0.04   0.971    -.2984934     .287696
             age |   .0268294   .0071969     3.73   0.000     .0127236    .0409351
   low_education |  -.7361641    .221467    -3.32   0.001    -1.170231   -.3020967
  high_education |   .4281272   .1641948     2.61   0.009     .1063114    .7499431
                 |
employmentstatus |
        Retired  |   .4089876   .3572734     1.14   0.252    -.2912554    1.109231
     Unemployed  |  -.4323618   .1868231    -2.31   0.021    -.7985283   -.0661954
        Student  |  -.1475226   .2601213    -0.57   0.571    -.6573509    .3623058
    Housechores  |  -.6203236   .4403449    -1.41   0.159    -1.483384    .2427365
                 |
           _cons |   .5681802   .3227861     1.76   0.078     -.064469    1.200829
----------------------------------------------------------------------------------

. margins, dydx( replacement) post

Average marginal effects                                 Number of obs = 1,638
Model VCE: OIM

Expression: Pr(vote_november), predict()
dy/dx wrt:  replacement

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
 replacement |   .0235628   .0189018     1.25   0.213    -.0134839    .0606096
------------------------------------------------------------------------------

. estimates store s4

. 
. 
. coefplot (s1 , label( "Turnout 2016 (Placebo)")) ( s2, label( "Turnout April 2019")) ///
>  ( s3, label( "Turnout May 2019 (Short-term effect)")) ( s4, label("Turnout November 2019 (Long-
> term effect)")) ///
>  , keep (replacement) vertical yline(0) ylabel(-0.04(.04).2) xlabel("")  title ("Models without 
> covariates") legend(position(6))  ytitle("ATE")

. 
. 
. 
.   graph copy g2

. 
.  graph combine g1 g2, title("ATE Soft treated: Replacement Officers")  

.  graph drop g1 g2

.  
. 
.  
.  
. ********************************************
. * MATCHINGS
. *****************************************
. 
. ****************************
. *******Matching 1
. ***************************
. 
. ****APRIL****
. 
. teffects psmatch (  vote_april) (treatment  female age low_education high_educat i.employmentsta
> tus  income_high income_med)

Treatment-effects estimation                   Number of obs      =      1,279
Estimator      : propensity-score matching     Matches: requested =          1
Outcome model  : matching                                     min =          1
Treatment model: logit                                        max =          7
------------------------------------------------------------------------------
             |              AI robust
  vote_april | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
ATE          |
   treatment |
   (1 vs 0)  |   .0685301    .025443     2.69   0.007     .0186628    .1183974
------------------------------------------------------------------------------

. 
. estimates store s1

. 
. teffects psmatch ( vote_apr) (treatment  female age low_education high_educat i.employmentstatus
>   income_high income_med) , nn(2)

Treatment-effects estimation                   Number of obs      =      1,279
Estimator      : propensity-score matching     Matches: requested =          2
Outcome model  : matching                                     min =          2
Treatment model: logit                                        max =          8
------------------------------------------------------------------------------
             |              AI robust
  vote_april | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
ATE          |
   treatment |
   (1 vs 0)  |   .0649075   .0227173     2.86   0.004     .0203823    .1094326
------------------------------------------------------------------------------

. 
. estimates store s2

. 
. coefplot (s1, label(PSM Nearest Neighbor:1) ) (s2,  label(PSM Nearest Neighbor:2)) , vertical yl
> ine(0) xlabel( 1 " ") ytitle("ATE") title ("April 2019") ///
>  legend(position (6) size(vsmall)) ylabel (-.08(.04).16)

.  graph copy g1

. 
. ****MAY****
. 
. 
. teffects psmatch ( vote_may) (treatment  female age low_education high_educat i.employmentstatus
>   income_high income_med)

Treatment-effects estimation                   Number of obs      =      1,279
Estimator      : propensity-score matching     Matches: requested =          1
Outcome model  : matching                                     min =          1
Treatment model: logit                                        max =          7
------------------------------------------------------------------------------
             |              AI robust
    vote_may | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
ATE          |
   treatment |
   (1 vs 0)  |   .0942403   .0328144     2.87   0.004     .0299252    .1585554
------------------------------------------------------------------------------

. 
. estimates store s1

. 
. teffects psmatch  ( vote_may) (treatment  female age low_education high_educat i.employmentstatu
> s  income_high income_med),   nn(2)

Treatment-effects estimation                   Number of obs      =      1,279
Estimator      : propensity-score matching     Matches: requested =          2
Outcome model  : matching                                     min =          2
Treatment model: logit                                        max =          8
------------------------------------------------------------------------------
             |              AI robust
    vote_may | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
ATE          |
   treatment |
   (1 vs 0)  |   .0676244   .0313809     2.15   0.031      .006119    .1291299
------------------------------------------------------------------------------

. 
. estimates store s2

. 
. 
. coefplot (s1, label(PSM Nearest Neighbor:1) ) (s2,  label(PSM Nearest Neighbor:2)) , vertical yl
> ine(0) xlabel( 1 " ") ytitle("ATE") title ("May 2019") ///
>   legend(position (6) size(vsmall)) ylabel (-.08(.04).16)

.  graph copy g2

. 
. ****NOVEMBER
. 
. 
. teffects psmatch  ( vote_nov) (treatment  female age low_education high_educat i.employmentstatu
> s  income_high income_med)

Treatment-effects estimation                   Number of obs      =      1,048
Estimator      : propensity-score matching     Matches: requested =          1
Outcome model  : matching                                     min =          1
Treatment model: logit                                        max =          7
------------------------------------------------------------------------------
             |              AI robust
vote_novem~r | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
ATE          |
   treatment |
   (1 vs 0)  |   .0005566   .0349025     0.02   0.987    -.0678511    .0689643
------------------------------------------------------------------------------

. 
. estimates store s1

. 
. teffects psmatch   ( vote_nov) (treatment  female age low_education high_educat i.employmentstat
> us  income_high income_med),   nn(2)

Treatment-effects estimation                   Number of obs      =      1,048
Estimator      : propensity-score matching     Matches: requested =          2
Outcome model  : matching                                     min =          2
Treatment model: logit                                        max =          7
------------------------------------------------------------------------------
             |              AI robust
vote_novem~r | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
ATE          |
   treatment |
   (1 vs 0)  |   -.030264   .0321896    -0.94   0.347    -.0933544    .0328264
------------------------------------------------------------------------------

. 
. estimates store s2

. 
. coefplot (s1, label(PSM Nearest Neighbor:1) ) (s2,  label(PSM Nearest Neighbor:2)) , vertical yl
> ine(0) xlabel( 1 " ") ytitle("ATE") title ("November 2019") ///
>  legend(position (6) size(vsmall)) ylabel (-.08(.04).16)

. 
. graph copy g3

. 
. 
. grc1leg  g1 g2 g3, legendfrom(g2) title ("Matching Estimates: Nearest Neighbor")

. 
. graph drop g1 g2 g3 

. 
. ***************************
. **Matching 2 
. ***************************
. 
. 
. ****APRIL****
. 
. teffects ipw ( vote_ap) (treatment  female age low_education high_educat i.employmentstatus  inc
> ome_high income_med)

Iteration 0:   EE criterion =  1.536e-14  
Iteration 1:   EE criterion =  1.646e-25  

Treatment-effects estimation                    Number of obs     =      1,279
Estimator      : inverse-probability weights
Outcome model  : weighted mean
Treatment model: logit
----------------------------------------------------------------------------------
                 |               Robust
      vote_april | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
ATE              |
       treatment |
       (1 vs 0)  |   .0795567   .0191204     4.16   0.000     .0420813     .117032
-----------------+----------------------------------------------------------------
POmean           |
       treatment |
              0  |   .8760394   .0102273    85.66   0.000     .8559942    .8960846
----------------------------------------------------------------------------------

. estimates store s3

. 
. 
. teffects aipw ( vote_ap) (treatment  female age low_education high_educat i.employmentstatus  in
> come_high income_med)

Iteration 0:   EE criterion =  1.536e-14  
Iteration 1:   EE criterion =  1.612e-25  

Treatment-effects estimation                    Number of obs     =      1,279
Estimator      : augmented IPW
Outcome model  : linear by ML
Treatment model: logit
----------------------------------------------------------------------------------
                 |               Robust
      vote_april | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
ATE              |
       treatment |
       (1 vs 0)  |   .0794787   .0191627     4.15   0.000     .0419206    .1170369
-----------------+----------------------------------------------------------------
POmean           |
       treatment |
              0  |   .8760397   .0102273    85.66   0.000     .8559946    .8960848
----------------------------------------------------------------------------------

. estimates store s4

. 
. 
. 
. coefplot  (s3,  label(Inv. Prob. Weighting)) ///
>  (s4,  label (Augm. Inv. Prob. Weighting)) , vertical yline(0) xlabel( 1 " ") ytitle("ATE") titl
> e ("April 2019") ///
>  legend(position (6) size(vsmall)) ylabel (-.08(.04).16)

.  graph copy g1

. 
. ****MAY****
. 
. teffects ipw ( vote_may) (treatment  female age low_education high_educat i.employmentstatus  in
> come_high income_med)

Iteration 0:   EE criterion =  1.536e-14  
Iteration 1:   EE criterion =  2.032e-25  

Treatment-effects estimation                    Number of obs     =      1,279
Estimator      : inverse-probability weights
Outcome model  : weighted mean
Treatment model: logit
----------------------------------------------------------------------------------
                 |               Robust
        vote_may | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
ATE              |
       treatment |
       (1 vs 0)  |   .0921899   .0243815     3.78   0.000     .0444031    .1399767
-----------------+----------------------------------------------------------------
POmean           |
       treatment |
              0  |   .8040907   .0122881    65.44   0.000     .7800064     .828175
----------------------------------------------------------------------------------

. estimates store s3

. 
. 
. teffects aipw  ( vote_may) (treatment  female age low_education high_educat i.employmentstatus  
> income_high income_med)

Iteration 0:   EE criterion =  1.536e-14  
Iteration 1:   EE criterion =  2.044e-25  

Treatment-effects estimation                    Number of obs     =      1,279
Estimator      : augmented IPW
Outcome model  : linear by ML
Treatment model: logit
----------------------------------------------------------------------------------
                 |               Robust
        vote_may | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
ATE              |
       treatment |
       (1 vs 0)  |   .0922108   .0243989     3.78   0.000     .0443897    .1400318
-----------------+----------------------------------------------------------------
POmean           |
       treatment |
              0  |   .8040911   .0122881    65.44   0.000     .7800069    .8281753
----------------------------------------------------------------------------------

. estimates store s4

. 
. coefplot  (s3,  label(Inv. Prob. Weighting)) ///
>  (s4,  label (Augm. Inv. Prob. Weighting)) , vertical yline(0) xlabel( 1 " ") ytitle("ATE") titl
> e ("May 2019") ///
>   legend(position (6) size(vsmall)) ylabel (-.08(.04).16)

.  graph copy g2

.  
.  
. ****NOVEMBER
. 
. teffects ipw  ( vote_nov) (treatment  female age low_education high_educat i.employmentstatus  i
> ncome_high income_med)

Iteration 0:   EE criterion =  1.302e-24  
Iteration 1:   EE criterion =  1.698e-33  

Treatment-effects estimation                    Number of obs     =      1,048
Estimator      : inverse-probability weights
Outcome model  : weighted mean
Treatment model: logit
----------------------------------------------------------------------------------
                 |               Robust
   vote_november | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
ATE              |
       treatment |
       (1 vs 0)  |   .0052467   .0280691     0.19   0.852    -.0497676    .0602611
-----------------+----------------------------------------------------------------
POmean           |
       treatment |
              0  |   .8425031   .0124871    67.47   0.000     .8180289    .8669773
----------------------------------------------------------------------------------

. estimates store s3

. 
. teffects aipw  ( vote_nov) (treatment  female age low_education high_educat i.employmentstatus  
> income_high income_med)

Iteration 0:   EE criterion =  1.302e-24  
Iteration 1:   EE criterion =  7.868e-33  

Treatment-effects estimation                    Number of obs     =      1,048
Estimator      : augmented IPW
Outcome model  : linear by ML
Treatment model: logit
----------------------------------------------------------------------------------
                 |               Robust
   vote_november | Coefficient  std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
ATE              |
       treatment |
       (1 vs 0)  |   .0052861   .0280786     0.19   0.851    -.0497469    .0603191
-----------------+----------------------------------------------------------------
POmean           |
       treatment |
              0  |   .8425032    .012487    67.47   0.000     .8180291    .8669774
----------------------------------------------------------------------------------

. estimates store s4

. coefplot  (s3,  label(Inv. Prob. Weighting)) ///
>  (s4,  label (Augm. Inv. Prob. Weighting)) , vertical yline(0) xlabel( 1 " ") ytitle("ATE") titl
> e ("November 2019") ///
>  legend(position (6) size(vsmall)) ylabel (-.08(.04).16)

. 
. graph copy g3

. grc1leg g1 g2 g3, legendfrom(g2) title ("Matching Estimates: Inverse Probability Weighting")

. graph drop g1 g2 g3 

. 
. 
. 
. 
. 
. 
.  *************************** 
. *MODELS WITHOUT COVARIATES + TURNOUT 2016
. ***************************
. 
. est clear

. 
. 
. 
. logit vote_april  treatment    vote_2016  

Iteration 0:   log likelihood = -549.99983  
Iteration 1:   log likelihood = -514.84181  
Iteration 2:   log likelihood = -496.51659  
Iteration 3:   log likelihood = -496.36351  
Iteration 4:   log likelihood = -496.36337  
Iteration 5:   log likelihood = -496.36337  

Logistic regression                                     Number of obs =  1,598
                                                        LR chi2(2)    = 107.27
                                                        Prob > chi2   = 0.0000
Log likelihood = -496.36337                             Pseudo R2     = 0.0975

------------------------------------------------------------------------------
  vote_april | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
   treatment |   1.251141   .3248978     3.85   0.000     .6143533    1.887929
   vote_2016 |   1.737694   .1781732     9.75   0.000     1.388481    2.086907
       _cons |   .6300747   .1479568     4.26   0.000     .3400848    .9200647
------------------------------------------------------------------------------

. margins, dydx(treatment) post

Average marginal effects                                 Number of obs = 1,598
Model VCE: OIM

Expression: Pr(vote_april), predict()
dy/dx wrt:  treatment

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
   treatment |   .1110841   .0290351     3.83   0.000     .0541763    .1679918
------------------------------------------------------------------------------

. estimates store s2

. 
. 
. logit   vote_may   treatment    vote_2016  

Iteration 0:   log likelihood = -747.73911  
Iteration 1:   log likelihood =  -710.2652  
Iteration 2:   log likelihood = -705.53667  
Iteration 3:   log likelihood = -705.53409  
Iteration 4:   log likelihood = -705.53409  

Logistic regression                                     Number of obs =  1,598
                                                        LR chi2(2)    =  84.41
                                                        Prob > chi2   = 0.0000
Log likelihood = -705.53409                             Pseudo R2     = 0.0564

------------------------------------------------------------------------------
    vote_may | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
   treatment |   .5532939   .2028906     2.73   0.006     .1556356    .9509521
   vote_2016 |   1.412917   .1568976     9.01   0.000     1.105404    1.720431
       _cons |    .313668   .1389653     2.26   0.024      .041301     .586035
------------------------------------------------------------------------------

. margins, dydx(treatment) post

Average marginal effects                                 Number of obs = 1,598
Model VCE: OIM

Expression: Pr(vote_may), predict()
dy/dx wrt:  treatment

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
   treatment |   .0758466   .0277395     2.73   0.006     .0214781    .1302151
------------------------------------------------------------------------------

. estimates store s3

. 
. 
. 
. logit   vote_nov  treatment    vote_2016     

Iteration 0:   log likelihood = -571.91926  
Iteration 1:   log likelihood = -545.16632  
Iteration 2:   log likelihood = -540.05191  
Iteration 3:   log likelihood = -540.05025  
Iteration 4:   log likelihood = -540.05025  

Logistic regression                                     Number of obs =  1,302
                                                        LR chi2(2)    =  63.74
                                                        Prob > chi2   = 0.0000
Log likelihood = -540.05025                             Pseudo R2     = 0.0557

-------------------------------------------------------------------------------
vote_november | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
    treatment |   .0762623   .2076909     0.37   0.713    -.3308045     .483329
    vote_2016 |   1.481025   .1781031     8.32   0.000     1.131949      1.8301
        _cons |    .461968   .1575704     2.93   0.003     .1531358    .7708003
-------------------------------------------------------------------------------

. margins, dydx(treatment) post

Average marginal effects                                 Number of obs = 1,302
Model VCE: OIM

Expression: Pr(vote_november), predict()
dy/dx wrt:  treatment

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
   treatment |   .0096242   .0262089     0.37   0.713    -.0417444    .0609928
------------------------------------------------------------------------------

. estimates store s4

. 
. 
. coefplot  ( s2, label( "Turnout April 2019")) ///
>  ( s3, label( "Turnout May 2019 (Short-term effect)")) ( s4, label("Turnout November 2019 (Long-
> term effect)")) ///
>  , keep ( treatment) vertical yline(0) ylabel(-0.04(.04).2)  xlabel("") title ("Models without c
> ovariates") legend(position(6))  ytitle("ATE") 

. 
.  graph copy g3

.  
. 
.  ***************************
. * MODELS WITH COVARIATES + TURNOUT 2016
. ***************************
. 
.  logit vote_april  treatment    vote_2016   female age low_education high_educat i.employmentsta
> tus  , 

Iteration 0:   log likelihood = -549.99983  
Iteration 1:   log likelihood =  -505.2532  
Iteration 2:   log likelihood =  -485.6537  
Iteration 3:   log likelihood = -485.44584  
Iteration 4:   log likelihood = -485.44565  
Iteration 5:   log likelihood = -485.44565  

Logistic regression                                     Number of obs =  1,598
                                                        LR chi2(10)   = 129.11
                                                        Prob > chi2   = 0.0000
Log likelihood = -485.44565                             Pseudo R2     = 0.1174

----------------------------------------------------------------------------------
      vote_april | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       treatment |   1.258922   .3290924     3.83   0.000     .6139128    1.903931
       vote_2016 |   1.723689   .1826005     9.44   0.000     1.365799     2.08158
          female |  -.0414555   .1804947    -0.23   0.818    -.3952185    .3123075
             age |   .0351291    .008806     3.99   0.000     .0178697    .0523885
   low_education |  -.3905577   .2797659    -1.40   0.163    -.9388889    .1577735
  high_education |    .264247   .1925519     1.37   0.170    -.1131477    .6416417
                 |
employmentstatus |
        Retired  |   -.650992   .3631921    -1.79   0.073    -1.362835    .0608513
     Unemployed  |  -.1723663   .2429765    -0.71   0.478    -.6485915    .3038589
        Student  |   .3346929    .306655     1.09   0.275    -.2663399    .9357256
    Housechores  |  -.2399654   .5548459    -0.43   0.665    -1.327443    .8475126
                 |
           _cons |    -.76504   .4032907    -1.90   0.058    -1.555475    .0253952
----------------------------------------------------------------------------------

. 
. 
. logit vote_april  treatment    vote_2016     female age low_education high_educat i.employmentst
> atus  , 

Iteration 0:   log likelihood = -549.99983  
Iteration 1:   log likelihood =  -505.2532  
Iteration 2:   log likelihood =  -485.6537  
Iteration 3:   log likelihood = -485.44584  
Iteration 4:   log likelihood = -485.44565  
Iteration 5:   log likelihood = -485.44565  

Logistic regression                                     Number of obs =  1,598
                                                        LR chi2(10)   = 129.11
                                                        Prob > chi2   = 0.0000
Log likelihood = -485.44565                             Pseudo R2     = 0.1174

----------------------------------------------------------------------------------
      vote_april | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       treatment |   1.258922   .3290924     3.83   0.000     .6139128    1.903931
       vote_2016 |   1.723689   .1826005     9.44   0.000     1.365799     2.08158
          female |  -.0414555   .1804947    -0.23   0.818    -.3952185    .3123075
             age |   .0351291    .008806     3.99   0.000     .0178697    .0523885
   low_education |  -.3905577   .2797659    -1.40   0.163    -.9388889    .1577735
  high_education |    .264247   .1925519     1.37   0.170    -.1131477    .6416417
                 |
employmentstatus |
        Retired  |   -.650992   .3631921    -1.79   0.073    -1.362835    .0608513
     Unemployed  |  -.1723663   .2429765    -0.71   0.478    -.6485915    .3038589
        Student  |   .3346929    .306655     1.09   0.275    -.2663399    .9357256
    Housechores  |  -.2399654   .5548459    -0.43   0.665    -1.327443    .8475126
                 |
           _cons |    -.76504   .4032907    -1.90   0.058    -1.555475    .0253952
----------------------------------------------------------------------------------

. margins, dydx(treatment) post

Average marginal effects                                 Number of obs = 1,598
Model VCE: OIM

Expression: Pr(vote_april), predict()
dy/dx wrt:  treatment

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
   treatment |   .1096146   .0288054     3.81   0.000     .0531569    .1660722
------------------------------------------------------------------------------

. estimates store s2

. 
. 
. logit   vote_may   treatment    vote_2016    female age low_education high_educat i.employmentst
> atus  , 

Iteration 0:   log likelihood = -747.73911  
Iteration 1:   log likelihood = -692.43939  
Iteration 2:   log likelihood =  -686.9328  
Iteration 3:   log likelihood = -686.92421  
Iteration 4:   log likelihood = -686.92421  

Logistic regression                                     Number of obs =  1,598
                                                        LR chi2(10)   = 121.63
                                                        Prob > chi2   = 0.0000
Log likelihood = -686.92421                             Pseudo R2     = 0.0813

----------------------------------------------------------------------------------
        vote_may | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       treatment |   .5948414   .2074035     2.87   0.004      .188338    1.001345
       vote_2016 |   1.381469   .1609671     8.58   0.000     1.065979    1.696959
          female |  -.0508426   .1459618    -0.35   0.728    -.3369225    .2352372
             age |   .0337497   .0071152     4.74   0.000     .0198043    .0476952
   low_education |  -.5029896   .2318813    -2.17   0.030    -.9574686   -.0485106
  high_education |    .252389   .1548074     1.63   0.103     -.051028    .5558059
                 |
employmentstatus |
        Retired  |  -.0589826   .3332872    -0.18   0.860    -.7122135    .5942483
     Unemployed  |  -.0406905    .203138    -0.20   0.841    -.4388336    .3574526
        Student  |   .2387501   .2467077     0.97   0.333    -.2447882    .7222883
    Housechores  |  -.8474182   .3977567    -2.13   0.033    -1.627007   -.0678293
                 |
           _cons |  -1.036396   .3375033    -3.07   0.002     -1.69789   -.3749019
----------------------------------------------------------------------------------

. margins, dydx(treatment) post

Average marginal effects                                 Number of obs = 1,598
Model VCE: OIM

Expression: Pr(vote_may), predict()
dy/dx wrt:  treatment

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
   treatment |   .0793819   .0275787     2.88   0.004     .0253285    .1334352
------------------------------------------------------------------------------

. estimates store s3

. 
. 
. 
. logit   vote_nov  treatment    vote_2016       female age low_education high_educat i.employment
> status  , 

Iteration 0:   log likelihood = -571.91926  
Iteration 1:   log likelihood = -524.27101  
Iteration 2:   log likelihood = -517.70237  
Iteration 3:   log likelihood = -517.68437  
Iteration 4:   log likelihood = -517.68436  

Logistic regression                                     Number of obs =  1,302
                                                        LR chi2(10)   = 108.47
                                                        Prob > chi2   = 0.0000
Log likelihood = -517.68436                             Pseudo R2     = 0.0948

----------------------------------------------------------------------------------
   vote_november | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       treatment |   .1003106   .2141526     0.47   0.639    -.3194207    .5200419
       vote_2016 |   1.451938   .1845837     7.87   0.000     1.090161    1.813716
          female |  -.0028553   .1726975    -0.02   0.987    -.3413361    .3356255
             age |   .0248618   .0081452     3.05   0.002     .0088975    .0408262
   low_education |   -.753168   .2622808    -2.87   0.004    -1.267229    -.239107
  high_education |   .2596598   .1825022     1.42   0.155     -.098038    .6173575
                 |
employmentstatus |
        Retired  |   .3980554   .4333216     0.92   0.358    -.4512393     1.24735
     Unemployed  |  -.4458968   .2197101    -2.03   0.042    -.8765207   -.0152728
        Student  |  -.3469375    .287918    -1.20   0.228    -.9112465    .2173715
    Housechores  |  -1.203039   .4145157    -2.90   0.004    -2.015475   -.3906037
                 |
           _cons |  -.4193849   .3910717    -1.07   0.284    -1.185871    .3471014
----------------------------------------------------------------------------------

. margins, dydx(treatment) post

Average marginal effects                                 Number of obs = 1,302
Model VCE: OIM

Expression: Pr(vote_november), predict()
dy/dx wrt:  treatment

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
   treatment |    .012146   .0259274     0.47   0.639    -.0386707    .0629628
------------------------------------------------------------------------------

. estimates store s4

. 
. 
. 
. coefplot( s2, label( "Turnout April 2019")) ///
>  ( s3, label( "Turnout May 2019 (Short-term effect)")) ( s4, label("Turnout November 2019 (Long-
> term effect)")) ///
>  , keep ( treatment) vertical yline(0) ylabel(-0.04(.04).2)  xlabel("") title ("Models without c
> ovariates") legend(position(6))  ytitle("ATE") 

. 
.  graph copy g4

.  
.  graph combine g3 g4

.  
.   graph drop  g3 g4

.  
. 
.  
.  ************************************
. **Probability to vote in November
. *************************************
. 
. reg prob_vote treatment

      Source |       SS           df       MS      Number of obs   =     1,558
-------------+----------------------------------   F(1, 1556)      =      0.02
       Model |  .099401205         1  .099401205   Prob > F        =    0.8968
    Residual |  9185.69071     1,556   5.9034002   R-squared       =    0.0000
-------------+----------------------------------   Adj R-squared   =   -0.0006
       Total |  9185.79012     1,557  5.89967252   Root MSE        =    2.4297

------------------------------------------------------------------------------
prob_voten~2 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
   treatment |   .0208913    .160998     0.13   0.897    -.2949046    .3366871
       _cons |   8.885246   .0678854   130.89   0.000     8.752089    9.018402
------------------------------------------------------------------------------

. estimates store A

. reg prob_vote  treatment  female age low_education high_educat i.employmentstatus  ,  

      Source |       SS           df       MS      Number of obs   =     1,558
-------------+----------------------------------   F(9, 1548)      =      4.09
       Model |  213.598932         9  23.7332147   Prob > F        =    0.0000
    Residual |  8972.19118     1,548  5.79598914   R-squared       =    0.0233
-------------+----------------------------------   Adj R-squared   =    0.0176
       Total |  9185.79012     1,557  5.89967252   Root MSE        =    2.4075

----------------------------------------------------------------------------------
prob_votenovem~2 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       treatment |   .0156302   .1619698     0.10   0.923    -.3020732    .3333335
          female |  -.3179775   .1301262    -2.44   0.015    -.5732197   -.0627353
             age |   .0179167   .0061957     2.89   0.004     .0057638    .0300696
   low_education |  -.4611137   .2308212    -2.00   0.046     -.913869   -.0083585
  high_education |   .2258399   .1341304     1.68   0.092    -.0372566    .4889363
                 |
employmentstatus |
        Retired  |  -.0220029   .2613635    -0.08   0.933    -.5346668    .4906611
     Unemployed  |  -.3095319   .1890318    -1.64   0.102    -.6803172    .0612534
        Student  |   .0950521    .238846     0.40   0.691    -.3734438     .563548
    Housechores  |  -1.569239   .4137584    -3.79   0.000    -2.380825   -.7576529
                 |
           _cons |   8.316092   .2862192    29.05   0.000     7.754674     8.87751
----------------------------------------------------------------------------------

. estimates store B

. reg prob_vote  treatment vote_2016  female age low_education high_educat i.employmentstatus  ,  

      Source |       SS           df       MS      Number of obs   =     1,558
-------------+----------------------------------   F(10, 1547)     =     16.82
       Model |  900.626202        10  90.0626202   Prob > F        =    0.0000
    Residual |  8285.16391     1,547  5.35563278   R-squared       =    0.0980
-------------+----------------------------------   Adj R-squared   =    0.0922
       Total |  9185.79012     1,557  5.89967252   Root MSE        =    2.3142

----------------------------------------------------------------------------------
prob_votenovem~2 | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
       treatment |  -.0305434   .1557487    -0.20   0.845    -.3360443    .2749574
       vote_2016 |   1.932596   .1706316    11.33   0.000     1.597903     2.26729
          female |  -.3638431   .1251508    -2.91   0.004    -.6093263   -.1183599
             age |   .0155287   .0059594     2.61   0.009     .0038393    .0272181
   low_education |  -.3944489   .2219576    -1.78   0.076    -.8298185    .0409207
  high_education |   .2751749   .1290079     2.13   0.033      .022126    .5282238
                 |
employmentstatus |
        Retired  |  -.1159684   .2513757    -0.46   0.645    -.6090415    .3771046
     Unemployed  |  -.2141229   .1819041    -1.18   0.239    -.5709276    .1426818
        Student  |   .0396815   .2296456     0.17   0.863    -.4107679     .490131
    Housechores  |   -1.51546   .3977584    -3.81   0.000    -2.295663   -.7352573
                 |
           _cons |   6.756392   .3076701    21.96   0.000     6.152897    7.359886
----------------------------------------------------------------------------------

. estimates store C

. 
. 
. coefplot  ( A, label( "Without covariates")) ///
>  ( B, label( "With covariates")) ( C, label( "With covariates + Turnout 2016")) ///
>  , keep (treatment) vertical yline(1) ylabel(0.6(.2)1.6) xlabel("")  title ("Probability to vote
>  in repeated election (0-10)")  legend(position(6)) eform  ytitle("Marginal Effect")

. 
.  
.  *******************
.  ***TREATMENT EFFECTS ON NOVEMBER ATTITUDES
. ***************************
. 
. reg d_voteimp_nov  voteimp_w1 vote_2016 treatment female age i.education i.employmentstatus   , 

      Source |       SS           df       MS      Number of obs   =     1,249
-------------+----------------------------------   F(16, 1232)     =     35.93
       Model |  4069.18751        16  254.324219   Prob > F        =    0.0000
    Residual |  8720.16557     1,232  7.07805647   R-squared       =    0.3182
-------------+----------------------------------   Adj R-squared   =    0.3093
       Total |  12789.3531     1,248  10.2478791   Root MSE        =    2.6605

----------------------------------------------------------------------------------
   d_voteimp_nov | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
      voteimp_w1 |  -.6029054   .0261838   -23.03   0.000    -.6542752   -.5515356
       vote_2016 |   .1953284   .2270422     0.86   0.390    -.2501038    .6407605
       treatment |  -.1179864   .1999643    -0.59   0.555    -.5102948    .2743219
          female |  -.2527543   .1645121    -1.54   0.125    -.5755092    .0700005
             age |   .0186485   .0077351     2.41   0.016     .0034731    .0338239
                 |
       education |
              2  |  -.3196676   1.207109    -0.26   0.791    -2.687885     2.04855
              3  |   .0617298   1.143329     0.05   0.957    -2.181358    2.304817
              4  |   .4586123   1.105191     0.41   0.678    -1.709652    2.626877
              5  |   .4872781   1.115019     0.44   0.662    -1.700269    2.674825
              6  |   .3383448    1.11292     0.30   0.761    -1.845084    2.521773
              7  |   .7283776   1.125543     0.65   0.518    -1.479816    2.936571
              8  |   .5104393    1.26407     0.40   0.686    -1.969529    2.990408
                 |
employmentstatus |
        Retired  |   .0773716   .3100592     0.25   0.803    -.5309308    .6856741
     Unemployed  |  -.1426328   .2318643    -0.62   0.539    -.5975254    .3122597
        Student  |   .3374361   .3133597     1.08   0.282    -.2773417    .9522138
    Housechores  |   .6756275   .5128345     1.32   0.188    -.3304982    1.681753
                 |
           _cons |   2.747162   1.148357     2.39   0.017       .49421    5.000115
----------------------------------------------------------------------------------

. estimates store A

. reg  d_voteduty_nov  voteduty_w1 vote_2016 treatment female age i.education i.employmentstatus  
>  , 

      Source |       SS           df       MS      Number of obs   =     1,261
-------------+----------------------------------   F(16, 1244)     =     27.16
       Model |  2011.92709        16  125.745443   Prob > F        =    0.0000
    Residual |  5758.78663     1,244   4.6292497   R-squared       =    0.2589
-------------+----------------------------------   Adj R-squared   =    0.2494
       Total |  7770.71372     1,260  6.16723311   Root MSE        =    2.1516

----------------------------------------------------------------------------------
  d_voteduty_nov | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
     voteduty_w1 |  -.4695283   .0234353   -20.04   0.000    -.5155054   -.4235511
       vote_2016 |   .3163451   .1856827     1.70   0.089    -.0479407    .6806309
       treatment |   .2628393   .1602058     1.64   0.101    -.0514641    .5771427
          female |  -.1374416     .13328    -1.03   0.303      -.39892    .1240368
             age |   .0109408   .0062145     1.76   0.079    -.0012511    .0231328
                 |
       education |
              2  |  -1.452728   .9765854    -1.49   0.137    -3.368665    .4632077
              3  |  -1.026448   .9236712    -1.11   0.267    -2.838573    .7856777
              4  |  -.5998646   .8916267    -0.67   0.501    -2.349123    1.149393
              5  |  -.7570492   .8993276    -0.84   0.400    -2.521416    1.007317
              6  |  -.8644467    .897402    -0.96   0.336    -2.625035    .8961418
              7  |  -.7917496    .907584    -0.87   0.383    -2.572314    .9888148
              8  |  -.0384069   1.019959    -0.04   0.970    -2.039437    1.962623
                 |
employmentstatus |
        Retired  |  -.1500164   .2509479    -0.60   0.550    -.6423443    .3423115
     Unemployed  |  -.3105159   .1871075    -1.66   0.097    -.6775969    .0565652
        Student  |   .0990017   .2506463     0.39   0.693    -.3927345    .5907379
    Housechores  |  -.4257697   .4134549    -1.03   0.303    -1.236916    .3853762
                 |
           _cons |   4.013652   .9381766     4.28   0.000     2.173069    5.854235
----------------------------------------------------------------------------------

. estimates store B

. reg  d_cleanelections_nov  cleanelections_w1 vote_2016 treatment female age i.education i.employ
> mentstatus   , 

      Source |       SS           df       MS      Number of obs   =     1,238
-------------+----------------------------------   F(16, 1221)     =     24.59
       Model |  2226.60795        16  139.162997   Prob > F        =    0.0000
    Residual |  6910.28058     1,221  5.65952546   R-squared       =    0.2437
-------------+----------------------------------   Adj R-squared   =    0.2338
       Total |  9136.88853     1,237  7.38632864   Root MSE        =     2.379

-----------------------------------------------------------------------------------
d_cleanelection~v | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
cleanelections_w1 |  -.4405997   .0226089   -19.49   0.000    -.4849563   -.3962432
        vote_2016 |    .238797   .1986655     1.20   0.230    -.1509666    .6285606
        treatment |   .2130758   .1805312     1.18   0.238      -.14111    .5672616
           female |    .103679   .1470728     0.70   0.481    -.1848644    .3922224
              age |   .0393572   .0070475     5.58   0.000     .0255306    .0531837
                  |
        education |
               2  |    .459242   1.083213     0.42   0.672    -1.665923    2.584407
               3  |    .413865   1.023587     0.40   0.686    -1.594319    2.422049
               4  |   .9975897   .9870585     1.01   0.312     -.938929    2.934108
               5  |   1.070691   .9959057     1.08   0.283     -.883185    3.024567
               6  |   1.271838   .9938426     1.28   0.201    -.6779907    3.221666
               7  |   1.304463   1.005776     1.30   0.195    -.6687778    3.277705
               8  |   1.566445   1.128785     1.39   0.165    -.6481268    3.781018
                  |
 employmentstatus |
         Retired  |   .1773026   .2774168     0.64   0.523    -.3669638     .721569
      Unemployed  |  -.2654017   .2103362    -1.26   0.207    -.6780621    .1472587
         Student  |  -.1158175    .282785    -0.41   0.682     -.670616    .4389809
     Housechores  |  -.6545594   .4721131    -1.39   0.166    -1.580802    .2716835
                  |
            _cons |   .0715034   1.027049     0.07   0.945    -1.943472    2.086479
-----------------------------------------------------------------------------------

. estimates store C

. reg  d_trustparties_nov trustparties_w1 vote_2016 treatment female age i.education i.employments
> tatus   , 

      Source |       SS           df       MS      Number of obs   =     1,253
-------------+----------------------------------   F(16, 1236)     =     31.10
       Model |  2108.44371        16  131.777732   Prob > F        =    0.0000
    Residual |  5237.22589     1,236  4.23723777   R-squared       =    0.2870
-------------+----------------------------------   Adj R-squared   =    0.2778
       Total |  7345.66959     1,252  5.86714824   Root MSE        =    2.0585

----------------------------------------------------------------------------------
d_trustparties~v | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
 trustparties_w1 |  -.5034463   .0228844   -22.00   0.000    -.5483428   -.4585497
       vote_2016 |   .3787504   .1708605     2.22   0.027     .0435417    .7139591
       treatment |  -.1897609   .1541283    -1.23   0.218     -.492143    .1126212
          female |  -.0253765   .1267942    -0.20   0.841    -.2741322    .2233791
             age |   .0051415   .0059801     0.86   0.390    -.0065907    .0168737
                 |
       education |
              2  |  -.5327452   .9340988    -0.57   0.569     -2.36534    1.299849
              3  |   -.547467   .8836342    -0.62   0.536    -2.281056    1.186122
              4  |  -.0298198   .8533476    -0.03   0.972     -1.70399     1.64435
              5  |  -.0107615   .8609025    -0.01   0.990    -1.699753     1.67823
              6  |  -.0821063   .8592252    -0.10   0.924    -1.767808    1.603595
              7  |    .086213   .8691724     0.10   0.921    -1.619004    1.791429
              8  |  -.3140671   .9753664    -0.32   0.748    -2.227624     1.59949
                 |
employmentstatus |
        Retired  |    .252202   .2399866     1.05   0.294    -.2186243    .7230282
     Unemployed  |   .2133052   .1804401     1.18   0.237    -.1406975    .5673079
        Student  |   .0872262   .2398286     0.36   0.716    -.3832899    .5577422
    Housechores  |   .2023964   .3960612     0.51   0.609    -.5746303     .979423
                 |
           _cons |   .8342785   .8881675     0.94   0.348    -.9082041    2.576761
----------------------------------------------------------------------------------

. estimates store D

. reg   d_votenotimp_nov  votenotimp_w1 vote_2016 treatment female age i.education i.employmentsta
> tus   , 

      Source |       SS           df       MS      Number of obs   =     1,259
-------------+----------------------------------   F(16, 1242)     =     36.17
       Model |  4641.95103        16  290.121939   Prob > F        =    0.0000
    Residual |  9960.84643     1,242  8.02000518   R-squared       =    0.3179
-------------+----------------------------------   Adj R-squared   =    0.3091
       Total |  14602.7975     1,258  11.6079471   Root MSE        =     2.832

----------------------------------------------------------------------------------
d_votenotimp_nov | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
   votenotimp_w1 |   -.599398   .0257867   -23.24   0.000    -.6499884   -.5488077
       vote_2016 |  -.2008918    .238198    -0.84   0.399    -.6682068    .2664231
       treatment |    .471995   .2118246     2.23   0.026     .0564215    .8875685
          female |    .138099   .1741891     0.79   0.428    -.2036384    .4798365
             age |   .0036022   .0081569     0.44   0.659    -.0124006    .0196051
                 |
       education |
              2  |     .63397   1.288658     0.49   0.623    -1.894218    3.162158
              3  |  -.1253533   1.215232    -0.10   0.918    -2.509488    2.258781
              4  |  -.7892877   1.173385    -0.67   0.501    -3.091324    1.512748
              5  |  -1.323187   1.183819    -1.12   0.264    -3.645694    .9993191
              6  |  -.8545693   1.181065    -0.72   0.469    -3.171672    1.462533
              7  |  -1.233647   1.194967    -1.03   0.302    -3.578024     1.11073
              8  |   .3714145   1.341893     0.28   0.782    -2.261214    3.004043
                 |
employmentstatus |
        Retired  |  -.6750341   .3312325    -2.04   0.042    -1.324871   -.0251971
     Unemployed  |   .0238034   .2466996     0.10   0.923    -.4601906    .5077974
        Student  |  -.5172642   .3305902    -1.56   0.118    -1.165841    .1313126
    Housechores  |   .2683536   .5451122     0.49   0.623    -.8010889    1.337796
                 |
           _cons |   2.871226   1.228462     2.34   0.020     .4611366    5.281315
----------------------------------------------------------------------------------

. estimates store E

. reg  d_citizensimp_nov citizensimp_w1 vote_2016 treatment female age i.education i.employmentsta
> tus   , 

      Source |       SS           df       MS      Number of obs   =     1,263
-------------+----------------------------------   F(16, 1246)     =     34.38
       Model |  4980.03277        16  311.252048   Prob > F        =    0.0000
    Residual |  11280.5864     1,246  9.05344012   R-squared       =    0.3063
-------------+----------------------------------   Adj R-squared   =    0.2974
       Total |  16260.6192     1,262  12.8848012   Root MSE        =    3.0089

----------------------------------------------------------------------------------
d_citizensimp_~v | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
  citizensimp_w1 |  -.6033502   .0259837   -23.22   0.000    -.6543269   -.5523735
       vote_2016 |   .3870645   .2489629     1.55   0.120    -.1013683    .8754972
       treatment |  -.0382095   .2249047    -0.17   0.865    -.4794432    .4030243
          female |  -.2509358   .1842361    -1.36   0.173    -.6123832    .1105115
             age |    .017043   .0086641     1.97   0.049     .0000452    .0340407
                 |
       education |
              2  |   .1167753    1.36413     0.09   0.932     -2.55947    2.793021
              3  |  -.7172472   1.292638    -0.55   0.579    -3.253234     1.81874
              4  |  -.3337263    1.24827    -0.27   0.789     -2.78267    2.115217
              5  |  -.8329362    1.25927    -0.66   0.508    -3.303459    1.637587
              6  |  -.7540945   1.256515    -0.60   0.549    -3.219213    1.711023
              7  |  -.6907829   1.270966    -0.54   0.587    -3.184253    1.802687
              8  |   -1.14666    1.42694    -0.80   0.422    -3.946131     1.65281
                 |
employmentstatus |
        Retired  |  -.0396926   .3494231    -0.11   0.910    -.7252153    .6458301
     Unemployed  |   .1307015   .2617215     0.50   0.618     -.382762     .644165
        Student  |   .5986192   .3521039     1.70   0.089    -.0921629    1.289401
    Housechores  |  -.4494391   .5870853    -0.77   0.444    -1.601224    .7023457
                 |
           _cons |   2.616027   1.298386     2.01   0.044     .0687624    5.163291
----------------------------------------------------------------------------------

. estimates store F

. coefplot  A, bylabel(Voting is important)  subtitle(, size(small))   ||  B, bylabel(Citizens are
>  important in Spanish politics) || C , bylabel(Elections are not fraudulent) /// 
> || E, bylabel(Voting does not change anything)  ||  D, bylabel(Trust in political parties)    ||
>  F , bylabel(Voting is a duty) || ///
> , keep( treatment) xline(0) ylabe(1 "ATE") level(95) xlabel (-.5 (0.5)1)

. 
. reg   d_freedomideas_nov   freedomideas_w1 vote_2016 treatment female age i.education i.employme
> ntstatus   , 

      Source |       SS           df       MS      Number of obs   =     1,249
-------------+----------------------------------   F(16, 1232)     =     31.36
       Model |    3726.484        16   232.90525   Prob > F        =    0.0000
    Residual |  9150.81624     1,232  7.42761059   R-squared       =    0.2894
-------------+----------------------------------   Adj R-squared   =    0.2802
       Total |  12877.3002     1,248  10.3183496   Root MSE        =    2.7254

----------------------------------------------------------------------------------
d_freedomideas~v | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
 freedomideas_w1 |  -.6142061   .0288989   -21.25   0.000    -.6709027   -.5575095
       vote_2016 |   .1244754   .2254499     0.55   0.581    -.3178329    .5667837
       treatment |   .0055715   .2039774     0.03   0.978      -.39461     .405753
          female |  -.1184503   .1676759    -0.71   0.480    -.4474122    .2105116
             age |   .0431529   .0079345     5.44   0.000     .0275863    .0587195
                 |
       education |
              2  |   3.635046   1.242818     2.92   0.004     1.196772     6.07332
              3  |   4.067943   1.171433     3.47   0.001     1.769718    6.366168
              4  |    3.93476   1.131637     3.48   0.001      1.71461    6.154909
              5  |   3.977513   1.142134     3.48   0.001      1.73677    6.218257
              6  |    3.74439   1.138952     3.29   0.001     1.509889     5.97889
              7  |     3.8719   1.152316     3.36   0.001     1.611181    6.132619
              8  |   3.444741   1.292705     2.66   0.008     .9085933    5.980889
                 |
employmentstatus |
        Retired  |    .282011   .3204523     0.88   0.379    -.3466817    .9107036
     Unemployed  |  -.1011194   .2376089    -0.43   0.670    -.5672823    .3650434
        Student  |   .1208745   .3210702     0.38   0.707    -.5090304    .7507793
    Housechores  |   .5112477   .5579501     0.92   0.360    -.5833899    1.605885
                 |
           _cons |  -1.444566   1.203122    -1.20   0.230     -3.80496    .9158286
----------------------------------------------------------------------------------

. estimates store G

. reg  d_corruption_nov corruption_w1 vote_2016 treatment female age i.education i.employmentstatu
> s   , 

      Source |       SS           df       MS      Number of obs   =     1,258
-------------+----------------------------------   F(16, 1241)     =     32.24
       Model |  2815.30153        16  175.956346   Prob > F        =    0.0000
    Residual |   6771.9894     1,241  5.45688107   R-squared       =    0.2936
-------------+----------------------------------   Adj R-squared   =    0.2845
       Total |  9587.29094     1,257  7.62712087   Root MSE        =     2.336

----------------------------------------------------------------------------------
d_corruption_nov | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-----------------+----------------------------------------------------------------
   corruption_w1 |  -.5555698   .0252996   -21.96   0.000    -.6052044   -.5059352
       vote_2016 |  -.4634998   .1924166    -2.41   0.016    -.8409976    -.086002
       treatment |  -.1536248   .1754182    -0.88   0.381    -.4977739    .1905243
          female |  -.1598671    .142908    -1.12   0.263    -.4402349    .1205008
             age |  -.0028977   .0067286    -0.43   0.667    -.0160985     .010303
                 |
       education |
              2  |   1.456744   1.061095     1.37   0.170    -.6249934    3.538481
              3  |   1.621661   1.003336     1.62   0.106    -.3467619    3.590084
              4  |   1.531867   .9685845     1.58   0.114    -.3683767    3.432111
              5  |   1.718107   .9768845     1.76   0.079     -.198421    3.634634
              6  |   1.594417   .9745998     1.64   0.102    -.3176282    3.506462
              7  |    1.53905   .9864864     1.56   0.119    -.3963153    3.474415
              8  |   2.355742   1.107791     2.13   0.034     .1823925    4.529092
                 |
employmentstatus |
        Retired  |  -.4535318   .2721985    -1.67   0.096    -.9875519    .0804883
     Unemployed  |   .5267877    .203999     2.58   0.010     .1265666    .9270088
        Student  |  -.0058993   .2754479    -0.02   0.983    -.5462944    .5344958
    Housechores  |   .7415351   .4690871     1.58   0.114    -.1787564    1.661826
                 |
           _cons |   2.730966   1.020099     2.68   0.008      .729656    4.732275
----------------------------------------------------------------------------------

. estimates store H

. reg  d_dontunderstandpolitics_nov dontunderstandpolitics_w1 vote_2016 treatment female age i.edu
> cation i.employmentstatus   , 

      Source |       SS           df       MS      Number of obs   =     1,253
-------------+----------------------------------   F(16, 1236)     =     31.79
       Model |  3094.12596        16  193.382872   Prob > F        =    0.0000
    Residual |  7517.85649     1,236  6.08240816   R-squared       =    0.2916
-------------+----------------------------------   Adj R-squared   =    0.2824
       Total |  10611.9824     1,252  8.47602431   Root MSE        =    2.4663

-------------------------------------------------------------------------------------------
d_dontunderstandpolitic~v | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
--------------------------+----------------------------------------------------------------
dontunderstandpolitics_w1 |  -.5566169   .0250515   -22.22   0.000     -.605765   -.5074688
                vote_2016 |  -.4057758   .2075887    -1.95   0.051     -.813041    .0014894
                treatment |   .2086799   .1845812     1.13   0.258    -.1534473    .5708071
                   female |  -.4029763   .1521536    -2.65   0.008    -.7014842   -.1044683
                      age |  -.0104758   .0071231    -1.47   0.142    -.0244505     .003499
                          |
                education |
                       2  |  -.0592813   1.116227    -0.05   0.958     -2.24919    2.130627
                       3  |  -.7358332   1.058365    -0.70   0.487    -2.812224    1.340557
                       4  |  -1.216245   1.021812    -1.19   0.234    -3.220923    .7884326
                       5  |  -1.188547   1.031233    -1.15   0.249    -3.211708    .8346147
                       6  |  -1.742275   1.029337    -1.69   0.091    -3.761715    .2771657
                       7  |  -1.872597   1.041361    -1.80   0.072    -3.915628    .1704335
                       8  |   -2.65596   1.169215    -2.27   0.023    -4.949827   -.3620938
                          |
         employmentstatus |
                 Retired  |   .0533367    .286371     0.19   0.852    -.5084903    .6151637
              Unemployed  |   .0813573   .2152211     0.38   0.705    -.3408818    .5035964
                 Student  |   .0500753   .2916703     0.17   0.864    -.5221483    .6222988
             Housechores  |   .8389726   .4741895     1.77   0.077    -.0913327    1.769278
                          |
                    _cons |   4.470302   1.074979     4.16   0.000     2.361317    6.579287
-------------------------------------------------------------------------------------------

. estimates store I

. reg  d_demonotworking_nov demonotworking_w1 vote_2016 treatment female age i.education i.employm
> entstatus   , 

      Source |       SS           df       MS      Number of obs   =     1,255
-------------+----------------------------------   F(16, 1238)     =     36.56
       Model |  4532.96903        16  283.310564   Prob > F        =    0.0000
    Residual |  9593.37838     1,238  7.74909401   R-squared       =    0.3209
-------------+----------------------------------   Adj R-squared   =    0.3121
       Total |  14126.3474     1,254  11.2650298   Root MSE        =    2.7837

-----------------------------------------------------------------------------------
d_demonotworkin~v | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
demonotworking_w1 |  -.6315016   .0263359   -23.98   0.000    -.6831696   -.5798336
        vote_2016 |  -.4513664    .229772    -1.96   0.050     -.902152   -.0005807
        treatment |   .0252114   .2082506     0.12   0.904    -.3833518    .4337746
           female |  -.2199674   .1707276    -1.29   0.198    -.5549147    .1149799
              age |  -.0167689   .0081228    -2.06   0.039    -.0327049   -.0008329
                  |
        education |
               2  |   -.231593   1.261309    -0.18   0.854    -2.706132    2.242946
               3  |  -.7611158   1.195692    -0.64   0.525    -3.106922     1.58469
               4  |  -.8311316   1.154072    -0.72   0.472    -3.095284    1.433021
               5  |   -1.00561   1.164148    -0.86   0.388     -3.28953     1.27831
               6  |  -.9731365    1.16158    -0.84   0.402     -3.25202    1.305747
               7  |  -1.050074   1.175206    -0.89   0.372     -3.35569    1.255541
               8  |  -1.088832   1.319562    -0.83   0.409    -3.677657    1.499994
                  |
 employmentstatus |
         Retired  |  -.3649764   .3244951    -1.12   0.261    -1.001598    .2716447
      Unemployed  |   .0280419    .242871     0.12   0.908    -.4484423    .5045261
         Student  |  -.0213892   .3272815    -0.07   0.948    -.6634769    .6206985
     Housechores  |   .5322322   .5273656     1.01   0.313    -.5023969    1.566861
                  |
            _cons |   6.214453   1.213328     5.12   0.000     3.834047    8.594859
-----------------------------------------------------------------------------------

. estimates store J

. reg  d_systnotworried_nov systnotworried_w1 vote_2016 treatment female age i.education i.employm
> entstatus   , 

      Source |       SS           df       MS      Number of obs   =     1,251
-------------+----------------------------------   F(16, 1234)     =     40.28
       Model |  4122.53309        16  257.658318   Prob > F        =    0.0000
    Residual |  7894.42775     1,234  6.39742929   R-squared       =    0.3431
-------------+----------------------------------   Adj R-squared   =    0.3345
       Total |  12016.9608     1,250  9.61356867   Root MSE        =    2.5293

-----------------------------------------------------------------------------------
d_systnotworrie~v | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
systnotworried_w1 |  -.6741015   .0268023   -25.15   0.000    -.7266846   -.6215183
        vote_2016 |  -.3024703   .2119245    -1.43   0.154    -.7182425     .113302
        treatment |   .0320816   .1896458     0.17   0.866    -.3399823    .4041456
           female |  -.0606378   .1552294    -0.39   0.696    -.3651805    .2439049
              age |   -.011596   .0073065    -1.59   0.113    -.0259306    .0027386
                  |
        education |
               2  |    .902065   1.151858     0.78   0.434    -1.357752    3.161882
               3  |   .7339152   1.085698     0.68   0.499    -1.396103    2.863934
               4  |   .4864101   1.048383     0.46   0.643      -1.5704     2.54322
               5  |   .1534773   1.057664     0.15   0.885    -1.921541    2.228496
               6  |  -.0325318   1.055735    -0.03   0.975    -2.103766    2.038702
               7  |   .1945067   1.068257     0.18   0.856    -1.901295    2.290308
               8  |   .9562182   1.198927     0.80   0.425    -1.395944     3.30838
                  |
 employmentstatus |
         Retired  |   .0808243   .2942777     0.27   0.784    -.4965156    .6581642
      Unemployed  |   .0368421    .220666     0.17   0.867      -.39608    .4697642
         Student  |  -.1008852   .2987742    -0.34   0.736    -.6870467    .4852763
     Housechores  |   .5747215   .4932325     1.17   0.244    -.3929456    1.542389
                  |
            _cons |   5.018133   1.115356     4.50   0.000     2.829929    7.206337
-----------------------------------------------------------------------------------

. estimates store K

. coefplot  G, bylabel(All ideas should be freely expressed) subtitle(, size(small)) || H, bylabel
> (Corruption is pervasive) || I, bylabel(I don't understand most political issues) || ///
>  J, bylabel(Democracy is not working well)  || K, bylabel(Political system does not care) ||  //
> /
> , keep( treatment) xline(0) ylabe(1 "ATE") level(95) xlabel (-.5 (0.5)1)

. 
. 
.  ***************
.  *HETEROGENEOUS EFFECTS
.  ***************+
.  
.  logit vote_may i.treatment##c.voteimp_w2  female age low_education high_educat i.employmentstat
> us  , 

Iteration 0:   log likelihood = -712.44954  
Iteration 1:   log likelihood = -665.12477  
Iteration 2:   log likelihood = -661.95696  
Iteration 3:   log likelihood = -661.94064  
Iteration 4:   log likelihood = -661.94064  

Logistic regression                                     Number of obs =  1,560
                                                        LR chi2(11)   = 101.02
                                                        Prob > chi2   = 0.0000
Log likelihood = -661.94064                             Pseudo R2     = 0.0709

----------------------------------------------------------------------------------------
              vote_may | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-----------------------+----------------------------------------------------------------
           1.treatment |   .0683829   .3952633     0.17   0.863     -.706319    .8430848
            voteimp_w2 |   .1531839   .0253981     6.03   0.000     .1034046    .2029632
                       |
treatment#c.voteimp_w2 |
                    1  |   .1041204   .0647464     1.61   0.108    -.0227803     .231021
                       |
                female |   .1282176   .1501097     0.85   0.393     -.165992    .4224273
                   age |   .0290734   .0072448     4.01   0.000      .014874    .0432729
         low_education |  -.4331133   .2425062    -1.79   0.074    -.9084168    .0421902
        high_education |   .1366792   .1563512     0.87   0.382    -.1697636     .443122
                       |
      employmentstatus |
              Retired  |   .0936714   .3433178     0.27   0.785    -.5792192     .766562
           Unemployed  |  -.0347318   .2073717    -0.17   0.867    -.4411728    .3717093
              Student  |   .2209421   .2571958     0.86   0.390    -.2831525    .7250367
          Housechores  |  -.8604485   .4038159    -2.13   0.033    -1.651913   -.0689839
                       |
                 _cons |  -.7263522   .3441078    -2.11   0.035    -1.400791   -.0519133
----------------------------------------------------------------------------------------

.  margins , dydx(treatment) at( voteimp_w2=(0(2)10)) 

Average marginal effects                                 Number of obs = 1,560
Model VCE: OIM

Expression: Pr(vote_may), predict()
dy/dx wrt:  1.treatment
1._at: voteimp_w2 =  0
2._at: voteimp_w2 =  2
3._at: voteimp_w2 =  4
4._at: voteimp_w2 =  6
5._at: voteimp_w2 =  8
6._at: voteimp_w2 = 10

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
0.treatment  |  (base outcome)
-------------+----------------------------------------------------------------
1.treatment  |
         _at |
          1  |   .0152049   .0873815     0.17   0.862    -.1560597    .1864695
          2  |   .0532853   .0547358     0.97   0.330    -.0539948    .1605654
          3  |   .0760836   .0325126     2.34   0.019     .0123601    .1398072
          4  |   .0843408   .0227586     3.71   0.000     .0397348    .1289468
          5  |   .0819612   .0202046     4.06   0.000      .042361    .1215614
          6  |    .073422   .0190608     3.85   0.000     .0360635    .1107805
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

.  marginsplot, yline(0) xtitle("Voting is important") ytitle("Conditional Treatment Effect") titl
> e("")

Variables that uniquely identify margins: voteimp_w2

.  graph copy interaction1

.   logit vote_may i.treatment##c.voteduty_w2   female age low_education high_educat i.employments
> tatus  , 

Iteration 0:   log likelihood = -724.52958  
Iteration 1:   log likelihood = -672.32636  
Iteration 2:   log likelihood = -667.92506  
Iteration 3:   log likelihood = -667.90558  
Iteration 4:   log likelihood = -667.90558  

Logistic regression                                     Number of obs =  1,574
                                                        LR chi2(11)   = 113.25
                                                        Prob > chi2   = 0.0000
Log likelihood = -667.90558                             Pseudo R2     = 0.0782

-----------------------------------------------------------------------------------------
               vote_may | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
------------------------+----------------------------------------------------------------
            1.treatment |   .1820932   .4657991     0.39   0.696    -.7308563    1.095043
            voteduty_w2 |   .1792751   .0255595     7.01   0.000     .1291794    .2293708
                        |
treatment#c.voteduty_w2 |
                     1  |   .0716108   .0607826     1.18   0.239    -.0475209    .1907426
                        |
                 female |   .1317664   .1489767     0.88   0.376    -.1602225    .4237553
                    age |   .0325236   .0072492     4.49   0.000     .0183154    .0467318
          low_education |  -.5138313    .239969    -2.14   0.032    -.9841618   -.0435008
         high_education |   .2289761   .1560277     1.47   0.142    -.0768325    .5347848
                        |
       employmentstatus |
               Retired  |   .0578983   .3373658     0.17   0.864    -.6033265    .7191231
            Unemployed  |  -.0327339   .2082347    -0.16   0.875    -.4408664    .3753987
               Student  |   .2598786   .2537063     1.02   0.306    -.2373765    .7571338
           Housechores  |  -.8739947   .3915147    -2.23   0.026    -1.641349     -.10664
                        |
                  _cons |  -1.375093   .3814435    -3.60   0.000    -2.122708   -.6274774
-----------------------------------------------------------------------------------------

.  margins , dydx(treatment) at(voteduty_w2 =(0(2)10)) 

Average marginal effects                                 Number of obs = 1,574
Model VCE: OIM

Expression: Pr(vote_may), predict()
dy/dx wrt:  1.treatment
1._at: voteduty_w2 =  0
2._at: voteduty_w2 =  2
3._at: voteduty_w2 =  4
4._at: voteduty_w2 =  6
5._at: voteduty_w2 =  8
6._at: voteduty_w2 = 10

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
0.treatment  |  (base outcome)
-------------+----------------------------------------------------------------
1.treatment  |
         _at |
          1  |   .0431992   .1098889     0.39   0.694     -.172179    .2585774
          2  |     .07158   .0774788     0.92   0.356    -.0802758    .2234357
          3  |   .0886958   .0486416     1.82   0.068      -.00664    .1840316
          4  |   .0933448   .0298081     3.13   0.002      .034922    .1517677
          5  |   .0880454   .0217517     4.05   0.000     .0454128     .130678
          6  |   .0768328   .0194027     3.96   0.000     .0388043    .1148613
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

.  marginsplot,  yline(0)  xtitle("Voting is a duty")  ytitle("Conditional Treatment Effect") titl
> e("")

Variables that uniquely identify margins: voteduty_w2

.  graph copy interaction2

.  logit vote_may i.treatment##c.citizensimp_w2   female age low_education high_educat i.employmen
> tstatus  , 

Iteration 0:   log likelihood = -728.24539  
Iteration 1:   log likelihood = -699.92767  
Iteration 2:   log likelihood = -698.60337  
Iteration 3:   log likelihood = -698.59864  
Iteration 4:   log likelihood = -698.59864  

Logistic regression                                     Number of obs =  1,569
                                                        LR chi2(11)   =  59.29
                                                        Prob > chi2   = 0.0000
Log likelihood = -698.59864                             Pseudo R2     = 0.0407

--------------------------------------------------------------------------------------------
                  vote_may | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
---------------------------+----------------------------------------------------------------
               1.treatment |   .0497178   .3690634     0.13   0.893    -.6736333    .7730688
            citizensimp_w2 |   .0439692   .0236686     1.86   0.063    -.0024204    .0903588
                           |
treatment#c.citizensimp_w2 |
                        1  |   .1141719   .0661614     1.73   0.084     -.015502    .2438459
                           |
                    female |   .0770448   .1455874     0.53   0.597    -.2083013    .3623909
                       age |   .0313846   .0069994     4.48   0.000      .017666    .0451032
             low_education |  -.5787015   .2291973    -2.52   0.012     -1.02792   -.1294829
            high_education |   .1670016   .1522809     1.10   0.273    -.1314636    .4654667
                           |
          employmentstatus |
                  Retired  |   .1459658   .3371884     0.43   0.665    -.5149113     .806843
               Unemployed  |  -.0244826   .2029247    -0.12   0.904    -.4222076    .3732425
                  Student  |   .1850496   .2464263     0.75   0.453    -.2979372    .6680364
              Housechores  |  -.7921096   .3777909    -2.10   0.036    -1.532566   -.0516531
                           |
                     _cons |  -.0955045   .3281113    -0.29   0.771    -.7385907    .5475817
--------------------------------------------------------------------------------------------

.  margins , dydx(treatment) at(citizensimp_w2 =(0(2)10))  

Average marginal effects                                 Number of obs = 1,569
Model VCE: OIM

Expression: Pr(vote_may), predict()
dy/dx wrt:  1.treatment
1._at: citizensimp_w2 =  0
2._at: citizensimp_w2 =  2
3._at: citizensimp_w2 =  4
4._at: citizensimp_w2 =  6
5._at: citizensimp_w2 =  8
6._at: citizensimp_w2 = 10

------------------------------------------------------------------------------
             |            Delta-method
             |      dy/dx   std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
0.treatment  |  (base outcome)
-------------+----------------------------------------------------------------
1.treatment  |
         _at |
          1  |   .0083652   .0615453     0.14   0.892    -.1122613    .1289917
          2  |   .0418331   .0384572     1.09   0.277    -.0335415    .1172078
          3  |   .0673705    .025134     2.68   0.007     .0181087    .1166323
          4  |   .0856951   .0218776     3.92   0.000     .0428157    .1285744
          5  |   .0978506   .0239574     4.08   0.000     .0508949    .1448063
          6  |   .1049851   .0268416     3.91   0.000     .0523765    .1575936
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

.   marginsplot, yline(0)  xtitle("Citizens are important in Spanish politics")  ytitle("Condition
> al Treatment Effect") title("")

Variables that uniquely identify margins: citizensimp_w2

.   graph copy interaction3

. 
.  graph combine interaction1 interaction2 interaction3

.  graph drop interaction1 interaction2 interaction3

. 
. 
. 
. 
. ***************************
. * MEDIATION Y CONTROL 
. ***************************
. 
. medeff (logit vote_april  treatment  ) (logit vote_may   treatment vote_april ), mediate( vote_a
> p ) treat(treatment) sims(500) 
Using 0 and 1 as treatment values

Iteration 0:   log likelihood = -549.99983
Iteration 1:   log likelihood = -540.48645
Iteration 2:   log likelihood = -539.66305
Iteration 3:   log likelihood = -539.64578
Iteration 4:   log likelihood = -539.64577

Logistic regression                               Number of obs   =       1598
                                                  LR chi2(1)      =      20.71
                                                  Prob > chi2     =     0.0000
Log likelihood = -539.64577                       Pseudo R2       =     0.0188

------------------------------------------------------------------------------
  vote_april | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
   treatment |   1.234121   .3188109     3.87   0.000      .609263    1.858979
       _cons |   1.958971   .0836661    23.41   0.000     1.794988    2.122953
------------------------------------------------------------------------------

Iteration 0:   log likelihood = -747.73911
Iteration 1:   log likelihood = -579.59544
Iteration 2:   log likelihood = -577.58947
Iteration 3:   log likelihood = -559.56883
Iteration 4:   log likelihood = -559.24523
Iteration 5:   log likelihood = -559.24473

Logistic regression                               Number of obs   =       1598
                                                  LR chi2(2)      =     376.99
                                                  Prob > chi2     =     0.0000
Log likelihood = -559.24473                       Pseudo R2       =     0.2521

------------------------------------------------------------------------------
    vote_may | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
   treatment |   .1523358   .2218269     0.69   0.492    -.2824369    .5871086
  vote_april |   3.495927   .2077873    16.82   0.000     3.088671    3.903182
       _cons |  -1.353768   .1877854    -7.21   0.000    -1.721821   -.9857152
------------------------------------------------------------------------------
(1,098 missing values generated)
(1,098 missing values generated)
(1,098 missing values generated)
------------------------------------------------------------------------------------
        Effect                 |  Mean           [95% Conf. Interval]
-------------------------------+----------------------------------------------------
        ACME1                  |  .0572181      .0353641      .0784438
        ACME0                  |  .0586023      .0357389      .0787559
        Direct Effect 1        |  .0144708     -.0314236      .0490591
        Direct Effect 0        |  .0158551     -.0326197      .0528537
        Total Effect           |  .0730732      .0214341      .1136791
        % of Total via ACME1   |  .7681285      .5024344      2.628298
        % of Total via ACME0   |  .7867112      .5145895      2.691883
            
        Average Mediation      |  .0579102      .0359271      .0790711
        Average Direct Effect  |   .015163     -.0317433      .0509564
        % of Tot Eff mediated  |  .7774199       .508512      2.660091
------------------------------------------------------------------------------------

. 
. medeff (logit vote_april  treatment  ) (logit  vote_nov    treatment vote_april), mediate( vote_
> april) treat(treatment) sims(500) 
Using 0 and 1 as treatment values

Iteration 0:   log likelihood =  -416.1829
Iteration 1:   log likelihood = -409.09733
Iteration 2:   log likelihood = -408.45956
Iteration 3:   log likelihood = -408.44524
Iteration 4:   log likelihood = -408.44523

Logistic regression                               Number of obs   =       1302
                                                  LR chi2(1)      =      15.48
                                                  Prob > chi2     =     0.0001
Log likelihood = -408.44523                       Pseudo R2       =     0.0186

------------------------------------------------------------------------------
  vote_april | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
   treatment |   1.242744   .3727694     3.33   0.001     .5121298    1.973359
       _cons |   2.080491   .0972249    21.40   0.000     1.889934    2.271049
------------------------------------------------------------------------------

Iteration 0:   log likelihood = -571.91926
Iteration 1:   log likelihood = -488.85102
Iteration 2:   log likelihood = -483.99814
Iteration 3:   log likelihood = -464.55538
Iteration 4:   log likelihood = -464.55143

Logistic regression                               Number of obs   =       1302
                                                  LR chi2(2)      =     214.74
                                                  Prob > chi2     =     0.0000
Log likelihood = -464.55143                       Pseudo R2       =     0.1877

------------------------------------------------------------------------------
vote_novem~r | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
   treatment |  -.3496006   .2199971    -1.59   0.112     -.780787    .0815857
  vote_april |   3.040829   .2188979    13.89   0.000     2.611797    3.469861
       _cons |  -.7932299   .1928649    -4.11   0.000    -1.171238   -.4152217
------------------------------------------------------------------------------
(802 missing values generated)
(802 missing values generated)
(802 missing values generated)
------------------------------------------------------------------------------------
        Effect                 |  Mean           [95% Conf. Interval]
-------------------------------+----------------------------------------------------
        ACME1                  |  .0459544      .0253171       .067058
        ACME0                  |  .0437403      .0235475      .0629778
        Direct Effect 1        | -.0356189     -.0900579      .0064251
        Direct Effect 0        | -.0378329     -.0950082      .0069433
        Total Effect           |  .0081215     -.0512565      .0556293
        % of Total via ACME1   |  1.362932     -22.34606      24.84695
        % of Total via ACME0   |  1.297268     -21.26946      23.64986
            
        Average Mediation      |  .0448474      .0244323      .0650687
        Average Direct Effect  | -.0367259     -.0925331      .0066842
        % of Tot Eff mediated  |    1.3301     -21.80776       24.2484
------------------------------------------------------------------------------------

. 
. 
. 
. 
. 
. 
. log close
      name:  <unnamed>
       log:  /Users/ignaciojurado/Dropbox/My Mac (MacBook-Pro.home)/Downloads/dataverse_files (1)/
> Replication_Appendix.log
  log type:  text
 closed on:  10 Jul 2023, 18:11:56
--------------------------------------------------------------------------------------------------
